Least Recently Used (LRU) Cache Problem LeetCode

/**
Design and implement a data structure for Least Recently Used (LRU) cache. It should support the following operations: get and put.

get(key) - Get the value (will always be positive) of the key if the key exists in the cache, otherwise return -1.
put(key, value) - Set or insert the value if the key is not already present. When the cache reached its capacity, it should invalidate the least recently used item before inserting a new item.

Follow up:
Could you do both operations in O(1) time complexity?

Example:

LRUCache cache = new LRUCache( 2 );

cache.put(1, 1);
cache.put(2, 2);
cache.get(1);       // returns 1
cache.put(3, 3);    // evicts key 2
cache.get(2);       // returns -1 (not found)
cache.put(4, 4);    // evicts key 1
cache.get(1);       // returns -1 (not found)
cache.get(3);       // returns 3
cache.get(4);       // returns 4
*/
import java.util.*;

class Node{
        Node prev;
        Node next;
        int key;
        int value;

        public Node(int key, int value) {
            this.key = key;
            this.value = value;
            this.prev = null;
            this.next = null;
        }
}
class LRUCache {
    private int capacity;
    private HashMap<Integer, Node> hs = new HashMap<Integer, Node>();
//two dummy nodes represent as head and tail
    private Node head = new Node(-1, -1);
    private Node tail = new Node(-1, -1);

    public LRUCache(int capacity) {
        this.capacity = capacity;
        tail.prev = head;
        head.next = tail;
    }

    public int get(int key) {
        if( !hs.containsKey(key)) {
            return -1;
        }

        // remove current
        Node current = hs.get(key);
        current.prev.next = current.next;
        current.next.prev = current.prev;

        // move current to tail
        move_to_tail(current);

        return hs.get(key).value;
    }

    public void put(int key, int value) {
        if( get(key) != -1) {
            hs.get(key).value = value;
            return;
        }

        if (hs.size() == capacity) {
            hs.remove(head.next.key);
            head.next = head.next.next;
            head.next.prev = head;
        }

        Node insert = new Node(key, value);
//put to map
        hs.put(key, insert);
//move fresh node to tail
        move_to_tail(insert);
    }

    private void move_to_tail(Node current) {
        current.prev = tail.prev;//tail is a dummy node so insert before tail
        tail.prev = current;
        current.prev.next = current;
        current.next = tail;
    }

/**
 * Your LRUCache object will be instantiated and called as such:
 * LRUCache obj = new LRUCache(capacity);
 * int param_1 = obj.get(key);
 * obj.put(key,value);
 */

}

public class LRUCacheDemo{
public static void main(String[] args){
LRUCache cache = new LRUCache( 2 );
cache.put(1, 1);
cache.put(2, 2);
System.out.println(cache.get(1));       // returns 1
cache.put(3, 3);    // evicts key 2
System.out.println(cache.get(2));       // returns -1 (not found)
cache.put(4, 4);    // evicts key 1
System.out.println(cache.get(1));       // returns -1 (not found)
System.out.println(cache.get(3));       // returns 3
System.out.println(cache.get(4));       // returns 4
}
}