human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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 $9$.

Includes members:
  Member "on local distributed sampling and counting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perfect sampling from spatial mixing")
  Member "a simple parallel and distributed sampling technique local glauber dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "local access to huge random objects through partial sampling")
  Member "what can be sampled locally":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed metropolis sampler with optimal parallelism")
  Member "sampling random colorings of sparse random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "counting solutions to random cnf formulas")
  Member "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spectral independence in high dimensional expanders and applications to the hardcore model")
  Member "on the complexity of local distributed graph problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")
  Member "the locality of distributed symmetry breaking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed maximum matching verification in congest")
  Member "deterministic distributed vertex coloring in polylogarithmic time":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "smaller cuts higher lower bounds")

Question: Please tell me which class the source paper 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)