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 "ignore or comply on breaking symmetry in consensus"):
  Abstract: We study consensus processes on the complete graph of $n$ nodes. Initially, each node supports one from up to n opinions. Nodes randomly and in parallel sample the opinions of constant many nodes. Based on these samples, they use an update rule to change their own opinion. #R##N#The goal is to reach consensus, a configuration where all nodes support the same opinion. We compare two well-known update rules: 2-Choices and 3-Majority. In the former, each node samples two nodes and adopts their opinion if they agree. In the latter, each node samples three nodes: If an opinion is supported by at least two samples the node adopts it, otherwise it randomly adopts one of the sampled opinions. Known results for these update rules focus on initial configurations with a limited number of colors (say $n^{1/3}$ ), or typically assume a bias, where one opinion has a much larger support than any other. For such biased configurations, the time to reach consensus is roughly the same for 2-Choices and 3-Majority. #R##N#Interestingly, we prove that this is no longer true for configurations with a large number of initial colors. In particular, we show that 3-Majority reaches consensus with high probability in $O(n^{3/4}\log^{7/8}n)$ rounds, while 2-Choices can need $\Omega(n/\log n)$ rounds. We thus get the first unconditional sublinear bound for 3-Majority and the first result separating the consensus time of these processes. Along the way, we develop a framework that allows a fine-grained comparison between consensus processes from a specific class. We believe that this framework might help to classify the performance of more consensus processes.

Includes members:
  Member "bounds on the voter model in dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics")
  Member "efficient k party voting with two choices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "noidy conmunixatipn on the convergence of the averaging population protocol")
  Member "stabilizing consensus with many opinions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the necessary memory to compute the plurality in multi agent systems")
  Member "the power of two choices in distributed voting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed voting in beep model")
  Member "plurality consensus in the gossip model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed voting in beep model")
  Member "simple dynamics for plurality consensus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed voting in beep model")
  Member "absorption time of the moran process":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "strong amplifiers of natural selection proofs")
  Member "coalescing random walks and voting on connected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "interactive particle systems on hypergraphs drift analysis and the walksat algorithm")

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.DC(Distributed, Parallel, and Cluster Computing)