human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "distributed voting in beep model"):
  with members:
    - "distributed voting ranking with optimal number of states per node"
    - "beeping a maximal independent set fast"
    - "deterministic rendezvous with detection using beeps"
    - "counting in one hop beeping networks"
    - "naming a channel with beeps"
    - "asynchronous broadcasting with bivalent beeps"
    - "feedback from nature simple randomised distributed algorithms for maximal independent set selection and greedy colouring"
    - "design patterns in beeping algorithms examples emulation and analysis"

Connected through hyperedge HE-2 (induced by source paper "communication cost of consensus for nodes with limited memory"):
  with members:
    - "global majority consensus by local majority polling on graphs of a given degree sequence"
    - "population protocols are fast"
    - "population protocols for leader election and exact majority with o log 2 n states and o log 2 n convergence time"
    - "space optimal majority in population protocols"
    - "time space trade offs in population protocols"
    - "discordant voting processes on finite graphs"
    - "stable leader election in population protocols requires linear time"
    - "determining majority in networks with local interactions and very small local memory"

Connected through hyperedge HE-3 (induced by source paper "on the necessary memory to compute the plurality in multi agent systems"):
  with members:
    - "distributed voting ranking with optimal number of states per node"
    - "tight analysis for the 3 majority consensus dynamics"
    - "time and space optimal counting in population protocols"
    - "stabilizing consensus with many opinions"
    - "determining majority in networks with local interactions and very small local memory"
    - "timing in chemical reaction networks"
    - "the computational power of population protocols"

Connected through hyperedge HE-4 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics"):
  with members:
    - "pooling or sampling collective dynamics for electrical flow estimation"
    - "phase transition of the 2 choices dynamics on core periphery networks"
    - "the ants problem"
    - "limits for rumor spreading in stochastic populations"
    - "ignore or comply on breaking symmetry in consensus"
    - "distributed graph clustering and sparsification"
    - "average whenever you meet opportunistic protocols for community detection"
    - "a tight analysis of the parallel undecided state dynamics with two colors"

Connected through hyperedge HE-5 (induced by source paper "consensus needs broadcast in noiseless models but can be exponentially easier in the presence of noise"):
  with members:
    - "ignore or comply on breaking symmetry in consensus"
    - "tight analysis for the 3 majority consensus dynamics"
    - "a tight analysis of the parallel undecided state dynamics with two colors"
    - "distributed computing with channel noise"
    - "breaking the log n barrier on rumor spreading"
    - "stabilizing consensus with many opinions"
    - "noisy rumor spreading and plurality consensus"
    - "the power of two choices in distributed voting"

Connected through hyperedge HE-6 (induced by source paper "phase transition of the 2 choices dynamics on core periphery networks"):
  with members:
    - "tight analysis for the 3 majority consensus dynamics"
    - "the coalescing branching random walk on expanders and the dual epidemic process"
    - "bounds on the voter model in dynamic networks"
    - "fast plurality consensus in regular expanders"
    - "discordant voting processes on finite graphs"
    - "stabilizing consensus with many opinions"
    - "core periphery in networks an axiomatic approach"
    - "the power of two choices in distributed voting"

Connected through hyperedge HE-7 (induced by source paper "almost logarithmic time space optimal leader election in population protocols"):
  with members:
    - "fast space optimal leader election in population protocols"
    - "logarithmic expected time leader election in population protocol model"
    - "space optimal majority in population protocols"
    - "time space trade offs in population protocols"
    - "time vs information tradeoffs for leader election in anonymous trees"
    - "on convergence and threshold properties of discrete lotka volterra population protocols"
    - "stable leader election in population protocols requires linear time"
    - "leader election for anonymous asynchronous agents in arbitrary networks"

Connected through hyperedge HE-8 (induced by source paper "collaboratively learning the best option on graphs using bounded local memory"):
  with members:
    - "asymptotic response time analysis for multi task parallel jobs"
    - "a distributed learning dynamics in social groups"
    - "delay asymptotics and bounds for multi task parallel jobs"
    - "simple dynamics for plurality consensus"
    - "the supermarket game"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Question: Please tell me which class the center node 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)