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 "on local distributed sampling and counting"):
  Abstract: In classic distributed graph problems, each instance on a graph specifies a space of feasible solutions (e.g. all proper ($\Delta+1$)-list-colorings of the graph), and the task of distributed algorithm is to construct a feasible solution using local information. #R##N#We study distributed sampling and counting problems, in which each instance specifies a joint distribution of feasible solutions. The task of distributed algorithm is to sample from this joint distribution, or to locally measure the volume of the probability space via the marginal probabilities. The latter task is also known as inference, which is a local counterpart of counting. #R##N#For self-reducible classes of instances, the following equivalences are established in the LOCAL model up to polylogarithmic factors: #R##N#$\bullet$ For all joint distributions, approximate inference and approximate sampling are computationally equivalent. #R##N#$\bullet$ For all joint distributions defined by local constraints, exact sampling is reducible to either one of the above tasks. #R##N#$\bullet$ If further, sequentially constructing a feasible solution is trivial locally, then all above tasks are easy if and only if the joint distribution exhibits strong spatial mixing. #R##N#Combining with the state of the arts of strong spatial mixing, we obtain efficient sampling algorithms in the LOCAL model for various important sampling problems, including: an $O(\sqrt{\Delta}\log^3n)$-round algorithm for exact sampling matchings in graphs with maximum degree $\Delta$, and an $O(\log^3n)$-round algorithm for sampling according to the hardcore model (weighted independent sets) in the uniqueness regime, which along with the $\Omega(\mathrm{diam})$ lower bound in arXiv:1702.00142 for sampling according to the hardcore model in the non-uniqueness regime, gives the first computational phase transition for distributed sampling.

Includes members:
  Member "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed metropolis sampler with optimal parallelism")
  Member "what can be sampled locally":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed metropolis sampler with optimal parallelism")
  Member "on derandomizing local distributed algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed maximum matching verification in congest")
  Member "uniform sampling through the lov asz local lemma":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic mcmc sampling")
  Member "on the complexity of local distributed graph problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")
  Member "counting hypergraph matchings up to uniqueness threshold":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the ising partition function zeros and deterministic approximation")
  Member "robust probabilistic inference in distributed systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximate decentralized bayesian inference")
  Member "correlation decay up to uniqueness in spin systems":
    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)