Problem: Given is a directed graph G with N vertices and M edges.
The vertices are numbered 1 to N, and the i-th edge is directed from Vertex A_i to Vertex B_i.
It is guaranteed that the graph contains no self-loops or multiple edges.

Determine whether there exists an induced subgraph (see Notes) of G such that the in-degree and out-degree of every vertex are both 1. If the answer is yes, show one such subgraph.
Here the null graph is not considered as a subgraph.

Constraints

* 1 \leq N \leq 1000
* 0 \leq M \leq 2000
* 1 \leq A_i,B_i \leq N
* A_i \neq B_i
* All pairs (A_i, B_i) are distinct.
* All values in input are integers.

Input

Input is given from Standard Input in the following format:


N M
A_1 B_1
A_2 B_2
:
A_M B_M


Output

If there is no induced subgraph of G that satisfies the condition, print `-1`. Otherwise, print an induced subgraph of G that satisfies the condition, in the following format:


K
v_1
v_2
:
v_K


This represents the induced subgraph of G with K vertices whose vertex set is \\{v_1, v_2, \ldots, v_K\\}. (The order of v_1, v_2, \ldots, v_K does not matter.) If there are multiple subgraphs of G that satisfy the condition, printing any of them is accepted.

Examples

Input

4 5
1 2
2 3
2 4
4 1
4 3


Output

3
1
2
4


Input

4 5
1 2
2 3
2 4
1 4
4 3


Output

-1


Input

6 9
1 2
2 3
3 4
4 5
5 6
5 1
5 2
6 1
6 2


Output

4
2
3
4
5

Solution (in PYTHON3):

Solution:
from collections import defaultdict
from heapq import heappop, heappush


class Graph(object):
    """
    隣接リストによる有向グラフ
    """

    def __init__(self):
        self.graph = defaultdict(list)

    def __len__(self):
        return len(self.graph)

    def add_edge(self, src, dst, weight=1):
        self.graph[src].append((dst, weight))

    def get_nodes(self):
        return self.graph.keys()


class Dijkstra(object):
    """
    ダイクストラ法（二分ヒープ）による最短経路探索
    計算量: O((E+V)logV)
    """

    def __init__(self, graph, start):
        self.g = graph.graph

        # startノードからの最短距離
        # startノードは0, それ以外は無限大で初期化
        self.dist = defaultdict(lambda: float('inf'))
        self.dist[start] = 0

        # 最短経路での1つ前のノード
        self.prev = defaultdict(lambda: None)

        # startノードをキューに入れる
        self.Q = []
        heappush(self.Q, (self.dist[start], start))

        while self.Q:
            # 優先度（距離）が最小であるキューを取り出す
            dist_u, u = heappop(self.Q)
            if self.dist[u] < dist_u:
                continue
            for v, weight in self.g[u]:
                alt = dist_u + weight
                if self.dist[v] > alt:
                    self.dist[v] = alt
                    self.prev[v] = u
                    heappush(self.Q, (alt, v))

    def shortest_distance(self, goal):
        """
        startノードからgoalノードまでの最短距離
        """
        return self.dist[goal]

    def shortest_path(self, goal):
        """
        startノードからgoalノードまでの最短経路
        """
        path = []
        node = goal
        while node is not None:
            path.append(node)
            node = self.prev[node]
        return path[::-1]


g = Graph()
N, M = [int(i) for i in input().split()]


for i in range(M):
    u, v = [int(i) for i in input().split()]
    g.add_edge(u-1, v-1, 1)
    g.add_edge(u-1+N, v-1, 1)
    g.add_edge(u-1, v-1+N, 1)

d = Dijkstra(g, 0)

ans_n = 10 ** 6
ans_index = -1
for i in range(N):
    d = Dijkstra(g, i)
    ans_n_ = d.dist[i+N]

    if ans_n_ < ans_n:
        ans_index = i
        ans_n = ans_n_

if ans_n == 10 ** 6:
    print(-1)
else:
    d = Dijkstra(g, ans_index)
    ans = d.shortest_path(ans_index + N)
    ans.sort()
    print(ans_n)
    for j in range(ans_n):
        print(ans[j]+1)