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 "plurality consensus in the gossip model"):
  Abstract: We study Plurality Consensus in the Gossip Model over a network of $n$ anonymous agents. Each agent supports an initial opinion or color. We assume that at the onset, the number of agents supporting the plurality color exceeds that of the agents supporting any other color by a sufficiently-large bias. The goal is to provide a protocol that, with high probability, brings the system into the configuration in which all agents support the (initial) plurality color. We consider the Undecided-State Dynamics, a well-known protocol which uses just one more state (the undecided one) than those necessary to store colors. We show that the speed of convergence of this protocol depends on the initial color configuration as a whole, not just on the gap between the plurality and the second largest color community. This dependence is best captured by a novel notion we introduce, namely, the monochromatic distance ${md}(\bar{\mathbf{c}})$ which measures the distance of the initial color configuration $\bar{ \mathbf {c}}$ from the closest monochromatic one. In the complete graph, we prove that, for a wide range of the input parameters, this dynamics converges within $O({md}(\bar {\mathbf {c}}) \log {n})$ rounds. We prove that this upper bound is almost tight in the strong sense: Starting from any color configuration $\bar {\mathbf {c}}$, the convergence time is $\Omega({md}(\bar {\mathbf {c}}))$. Finally, we adapt the Undecided-State Dynamics to obtain a fast, random walk-based protocol for plurality consensus on regular expanders. This protocol converges in $O({md}(\bar {\mathbf {c}}) \mathrm{polylog}(n))$ rounds using only $\mathrm{polylog}(n)$ local memory. A key-ingredient to achieve the above bounds is a new analysis of the maximum node congestion that results from performing $n$ parallel random walks on regular expanders. All our bounds hold with high probability.

Includes members:
  Member "the power of two choices in distributed voting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed voting in beep model")
  Member "determining majority in networks with local interactions and very small local memory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust comparison in population protocols")
  Member "rumor spreading in random evolving graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the radius of nonsplit graphs and information dissemination in dynamic networks")
  Member "distributed community detection in dynamic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient distributed community detection in the stochastic block model")
  Member "simple dynamics for plurality consensus":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed voting in beep model")
  Member "timing in chemical reaction networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "communication cost of consensus for nodes with limited memory")
  Member "consensus on the initial global majority by local majority polling for a class of sparse graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the parallel undecided state dynamics with two colors")
  Member "near optimal random walk sampling in distributed networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed random walks")

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)