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 metropolis sampler with optimal parallelism"):
  Abstract: The Metropolis-Hastings algorithm is a fundamental Markov chain Monte Carlo (MCMC) method for sampling and inference. With the advent of Big Data, distributed and parallel variants of MCMC methods are attracting increased attention. In this paper, we give a distributed algorithm that can correctly simulate sequential single-site Metropolis chains without any bias in a fully asynchronous message-passing model. Furthermore, if a natural Lipschitz condition is satisfied by the Metropolis filters, our algorithm can simulate $N$-step Metropolis chains within $O(N/n+\log n)$ rounds of asynchronous communications, where $n$ is the number of variables. For sequential single-site dynamics, whose mixing requires $\Omega(n\log n)$ steps, this achieves an optimal linear speedup. For several well-studied important graphical models, including proper graph coloring, hardcore model, and Ising model, our condition for linear speedup is weaker than the respective uniqueness (mixing) conditions.  The novel idea in our algorithm is to resolve updates in advance: the local Metropolis filters can often be executed correctly before the full information about neighboring spins is available. This achieves optimal parallelism without introducing any bias.

Includes members:
  Member "dynamic sampling from graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perfect sampling from spatial mixing")
  Member "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fully asynchronous distributed metropolis sampler with optimal speedup")
  Member "a simple parallel and distributed sampling technique local glauber dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "local access to huge random objects through partial sampling")
  Member "hogwild gibbs can be panaccurate":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regression from dependent observations")
  Member "what can be sampled locally":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fully asynchronous distributed metropolis sampler with optimal speedup")
  Member "ensuring rapid mixing and low bias for asynchronous gibbs sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fully asynchronous distributed metropolis sampler with optimal speedup")
  Member "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fully asynchronous distributed metropolis sampler with optimal speedup")
  Member "computational transition at the uniqueness threshold":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spectral independence in high dimensional expanders and applications to the hardcore model")

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)