human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"
  Abstract: We examine the problem of almost-uniform sampling proper $q$-colorings of a graph whose maximum degree is $\Delta$. A famous result, discovered independently by Jerrum(1995) and Salas and Sokal(1997), is that, assuming $q > (2+\delta) \Delta$, the Glauber dynamics (a.k.a.~single-site dynamics) for this problem has mixing time $O(n \log n)$, where $n$ is the number of vertices, and thus provides a nearly linear time sampling algorithm for this problem. A natural question is the extent to which this algorithm can be parallelized. Previous work Feng, Sun and Yin [PODC'17] has shown that a $O(\Delta \log n)$ time parallelized algorithm is possible, and that $\Omega(\log n)$ time is necessary. #R##N#We give a distributed sampling algorithm, which we call the Lazy Local Metropolis Algorithm, that achieves an optimal parallelization of this classic algorithm. It improves its predecessor, the Local Metropolis algorithm of Feng, Sun and Yin [PODC'17], by introducing a step of distributed symmetry breaking that helps the mixing of the distributed sampling algorithm. #R##N#For sampling almost-uniform proper $q$-colorings of graphs $G$ on $n$ vertices, we show that the Lazy Local Metropolis algorithm achieves an optimal $O(\log n)$ mixing time if either of the following conditions is true for an arbitrary constant $\delta>0$: #R##N#(1) $q\ge(2+\delta)\Delta$, on general graphs with maximum degree $\Delta$; #R##N#(2) $q \geq (\alpha^* + \delta)\Delta$, where $\alpha^* \approx 1.763$ satisfies $\alpha^* = \mathrm{e}^{1/\alpha^*}$, on graphs with sufficiently large maximum degree $\Delta\ge \Delta_0(\delta)$ and girth at least $9$.

Connected through hyperedge HE-1 (induced by source paper "distributed metropolis sampler with optimal parallelism"):
  with members:
    - "dynamic sampling from graphical models"
    - "a simple parallel and distributed sampling technique local glauber dynamics"
    - "hogwild gibbs can be panaccurate"
    - "what can be sampled locally"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-2 (induced by source paper "fully asynchronous distributed metropolis sampler with optimal speedup"):
  with members:
    - "a simple parallel and distributed sampling technique local glauber dynamics"
    - "what can be sampled locally"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-3 (induced by source paper "dynamic sampling from graphical models"):
  with members:
    - "a simple parallel and distributed sampling technique local glauber dynamics"
    - "tight bounds for popping algorithms"
    - "sublogarithmic distributed algorithms for lov asz local lemma and the complexity hierarchy"
    - "what can be sampled locally"
    - "a polynomial time approximation algorithm for all terminal network reliability"
    - "uniform sampling through the lov asz local lemma"
    - "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model"
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"

Connected through hyperedge HE-4 (induced by source paper "on local distributed sampling and counting"):
  with members:
    - "what can be sampled locally"
    - "on derandomizing local distributed algorithms"
    - "uniform sampling through the lov asz local lemma"
    - "on the complexity of local distributed graph problems"
    - "counting hypergraph matchings up to uniqueness threshold"
    - "robust probabilistic inference in distributed systems"
    - "correlation decay up to uniqueness in spin systems"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-5 (induced by source paper "a simple parallel and distributed sampling technique local glauber dynamics"):
  with members:
    - "rapid mixing of glauber dynamics for colorings below vigoda s 11 6 threshold"
    - "linear programming bounds for randomly sampling colorings"
    - "what can be sampled locally"
    - "uniform sampling through the lov asz local lemma"

Connected through hyperedge HE-6 (induced by source paper "rapid mixing of glauber dynamics for colorings below vigoda s 11 6 threshold"):
  with members:
    - "sampling random colorings of sparse random graphs"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.DS(Data Structures and Algorithms)