use std::cmp::Ordering;
use std::cmp::{max, min};

use std::collections::BinaryHeap;
use std::collections::VecDeque;
use std::usize;

fn run() {
    let (r, w) = (std::io::stdin(), std::io::stdout());
    let mut sc = IO::new(r.lock(), w.lock());

    let n: usize = sc.read();
    let m: usize = sc.read();
    let ab = sc.read_pairs_1_indexed(m);

    let mut graph = vec![vec![]; n];
    for &(a, b) in ab.iter() {
        graph[a].push(b);
        graph[b].push(a);
    }

    let mut dist = vec![n + 1; n];
    dist[0] = 0;
    let mut prev = vec![0; n];
    let mut queue = VecDeque::new();
    queue.push_back(0);
    while let Some(cur) = queue.pop_front() {
        for &next in graph[cur].iter() {
            if dist[next] > dist[cur] + 1 {
                dist[next] = dist[cur] + 1;
                prev[next] = cur;
                queue.push_back(next);
            }
        }
    }

    println!("Yes");
    for p in prev.into_iter().skip(1) {
        println!("{}", p + 1);
    }
}

fn main() {
    std::thread::Builder::new()
        .name("run".into())
        .stack_size(256 * 1024 * 1024)
        .spawn(run)
        .unwrap()
        .join()
        .unwrap();
}

#[derive(Copy, Clone, Eq, PartialEq)]
struct State {
    cost: usize,
    position: usize,
}

// The priority queue depends on `Ord`.
// Explicitly implement the trait so the queue becomes a min-heap
// instead of a max-heap.
impl Ord for State {
    fn cmp(&self, other: &State) -> Ordering {
        // Notice that the we flip the ordering on costs.
        // In case of a tie we compare positions - this step is necessary
        // to make implementations of `PartialEq` and `Ord` consistent.
        other
            .cost
            .cmp(&self.cost)
            .then_with(|| self.position.cmp(&other.position))
    }
}

// `PartialOrd` needs to be implemented as well.
impl PartialOrd for State {
    fn partial_cmp(&self, other: &State) -> Option<Ordering> {
        Some(self.cmp(other))
    }
}

// Each node is represented as an `usize`, for a shorter implementation.
struct Edge {
    node: usize,
    cost: usize,
}

// Dijkstra's shortest path algorithm.

// Start at `start` and use `dist` to track the current shortest distance
// to each node. This implementation isn't memory-efficient as it may leave duplicate
// nodes in the queue. It also uses `usize::MAX` as a sentinel value,
// for a simpler implementation.
fn dijkstra(adj_list: &Vec<Vec<Edge>>, start: usize, goal: usize) -> Option<usize> {
    // dist[node] = current shortest distance from `start` to `node`
    let mut dist: Vec<_> = (0..adj_list.len()).map(|_| usize::MAX).collect();

    let mut heap = BinaryHeap::new();

    // We're at `start`, with a zero cost
    dist[start] = 0;
    heap.push(State {
        cost: 0,
        position: start,
    });

    // Examine the frontier with lower cost nodes first (min-heap)
    while let Some(State { cost, position }) = heap.pop() {
        // Alternatively we could have continued to find all shortest paths
        if position == goal {
            return Some(cost);
        }

        // Important as we may have already found a better way
        if cost > dist[position] {
            continue;
        }

        // For each node we can reach, see if we can find a way with
        // a lower cost going through this node
        for edge in &adj_list[position] {
            let next = State {
                cost: cost + edge.cost,
                position: edge.node,
            };

            // If so, add it to the frontier and continue
            if next.cost < dist[next.position] {
                heap.push(next);
                // Relaxation, we have now found a better way
                dist[next.position] = next.cost;
            }
        }
    }

    // Goal not reachable
    None
}

pub struct IO<R, W: std::io::Write>(R, std::io::BufWriter<W>);

impl<R: std::io::Read, W: std::io::Write> IO<R, W> {
    pub fn new(r: R, w: W) -> IO<R, W> {
        IO(r, std::io::BufWriter::new(w))
    }
    pub fn write<S: std::ops::Deref<Target = str>>(&mut self, s: S) {
        use std::io::Write;
        self.1.write(s.as_bytes()).unwrap();
    }
    pub fn read<T: std::str::FromStr>(&mut self) -> T {
        use std::io::Read;
        let buf = self
            .0
            .by_ref()
            .bytes()
            .map(|b| b.unwrap())
            .skip_while(|&b| b == b' ' || b == b'\n' || b == b'\r' || b == b'\t')
            .take_while(|&b| b != b' ' && b != b'\n' && b != b'\r' && b != b'\t')
            .collect::<Vec<_>>();
        unsafe { std::str::from_utf8_unchecked(&buf) }
            .parse()
            .ok()
            .expect("Parse error.")
    }
    pub fn read_vec<T: std::str::FromStr>(&mut self, n: usize) -> Vec<T> {
        (0..n).map(|_| self.read()).collect()
    }
    pub fn read_pairs<T: std::str::FromStr>(&mut self, n: usize) -> Vec<(T, T)> {
        (0..n).map(|_| (self.read(), self.read())).collect()
    }
    pub fn read_pairs_1_indexed(&mut self, n: usize) -> Vec<(usize, usize)> {
        (0..n)
            .map(|_| (self.read::<usize>() - 1, self.read::<usize>() - 1))
            .collect()
    }
    pub fn read_chars(&mut self) -> Vec<char> {
        self.read::<String>().chars().collect()
    }
    pub fn read_char_grid(&mut self, n: usize) -> Vec<Vec<char>> {
        (0..n).map(|_| self.read_chars()).collect()
    }
    pub fn read_matrix<T: std::str::FromStr>(&mut self, n: usize, m: usize) -> Vec<Vec<T>> {
        (0..n)
            .map(|_| (0..m).map(|_| self.read()).collect())
            .collect()
    }
}

#[allow(dead_code)]
fn yn(flag: bool) {
    if flag {
        println!("Yes");
    } else {
        println!("No");
    }
}

use std::ops::{Add, Div};

/// Generic binary search
///
/// # Arguments
///
/// * `l` - the value assumes to `pred(l) = true`
/// * `r` - the value assumes to `pred(r) = false`
/// * `pred` - predicate for binary search
///
/// # Returns
///
/// * `ret` where `pred(ret) = true` && `pred(ret + delta) = false`
///
/// # Note
///
/// `pred(l)` and `pred(r)` are not called. `pred` is called only values in the range `(l, r)`.
///
pub fn binary_search<T: Add<Output = T> + Div<Output = T> + PartialEq + From<u8> + Copy>(
    l: T,
    r: T,
    pred: impl Fn(T) -> bool,
) -> T {
    let mut l = l;
    let mut r = r;
    let two = T::from(2_u8);
    loop {
        let m = (l + r) / two;
        if l == m || r == m {
            break l;
        }
        if pred(m) {
            l = m;
        } else {
            r = m;
        }
    }
}

#[test]
fn binary_search_test() {
    let v = [1, 2, 3, 4, 5];
    assert_eq!(3, binary_search(v.len() as _, -1, |i| v[i as usize] > 3));
    assert_eq!(5, binary_search(v.len() as _, -1, |i| v[i as usize] > 100));
    assert_eq!(0, binary_search(v.len() as _, -1, |i| v[i as usize] > 0));
}

/// Returns index to the first element in `v` which does not compare less than `val`.
pub fn lower_bound<T: PartialOrd>(v: &[T], val: &T) -> usize {
    (binary_search(-1, v.len() as i64, |i: i64| v[i as usize] < *val) + 1) as usize
}

#[test]
fn lower_bound_test() {
    let v = vec![1, 3, 3, 4, 5];
    assert_eq!(lower_bound(&v, &0), 0);
    assert_eq!(lower_bound(&v, &1), 0);
    assert_eq!(lower_bound(&v, &2), 1);
    assert_eq!(lower_bound(&v, &3), 1);
    assert_eq!(lower_bound(&v, &4), 3);
    assert_eq!(lower_bound(&v, &5), 4);
    assert_eq!(lower_bound(&v, &999), 5);
}

/// Returns index to the first element in `v` which compares greater than `val`.
pub fn upper_bound<T: PartialOrd>(v: &[T], val: &T) -> usize {
    (binary_search(-1, v.len() as i64, |i: i64| v[i as usize] <= *val) + 1) as usize
}

#[test]
fn upper_bound_test() {
    let v: &[i32] = &[1, 3, 3, 4, 5];
    assert_eq!(upper_bound(v, &0), 0);
    assert_eq!(upper_bound(v, &1), 1);
    assert_eq!(upper_bound(v, &2), 1);
    assert_eq!(upper_bound(v, &3), 3);
    assert_eq!(upper_bound(v, &4), 4);
    assert_eq!(upper_bound(v, &5), 5);
    assert_eq!(upper_bound(v, &999), 5);
}

/// Returns `(lower_bound(v, val), upper_bound(v, val))`
pub fn equal_range<T: PartialOrd>(v: &[T], val: &T) -> (usize, usize) {
    (lower_bound(v, val), upper_bound(v, val))
}

pub fn gcd(a: usize, b: usize) -> usize {
    if b == 0 {
        a
    } else {
        gcd(b, a % b)
    }
}


Measured memory_bytes: 14640