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: "fast plurality consensus in regular expanders"
  Abstract: Pull voting is a classic method to reach consensus among $n$ vertices with differing opinions in a distributed network: each vertex at each step takes on the opinion of a random neighbour. This method, however, suffers from two drawbacks. Even if there are only two opposing opinions, the time taken for a single opinion to emerge can be slow and the final opinion is not necessarily the initially held majority. #R##N#We refer to a protocol where 2 neighbours are contacted at each step as a 2-sample voting protocol. In the two-sample protocol a vertex updates its opinion only if both sampled opinions are the same. Not much was known about the performance of two-sample voting on general expanders in the case of three or more opinions. In this paper we show that the following performance can be achieved on a $d$-regular expander using two-sample voting. We suppose there are $k \ge 3$ opinions, and that the initial size of the largest and second largest opinions is $A_1, A_2$ respectively. #R##N#We prove that, if $A_1 - A_2 \ge C n \max\{\sqrt{(\log n)/A_1}, \lambda\}$, where $\lambda$ is the absolute second eigenvalue of matrix $P=Adj(G)/d$ and $C$ is a suitable constant, then the largest opinion wins in $O((n \log n)/A_1)$ steps with high probability. #R##N#For almost all $d$-regular graphs, we have $\lambda=c/\sqrt{d}$ for some constant $c>0$. This means that as $d$ increases we can separate an opinion whose majority is $o(n)$, whereas $\Theta(n)$ majority is required for $d$ constant. #R##N#This work generalizes the results of Becchetti et. al (SPAA 2014) for the complete graph $K_n$.

Connected through hyperedge HE-1 (induced by source paper "best of three voting on dense graphs"):
  with members:
    - "global majority consensus by local majority polling on graphs of a given degree sequence"
    - "better bounds for coalescing branching random walks"
    - "the power of two choices in distributed voting"
    - "simple dynamics for plurality consensus"

Connected through hyperedge HE-2 (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-3 (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"
    - "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"
    - "plurality consensus in the gossip model"

Connected through hyperedge HE-4 (induced by source paper "on coalescence time in graphs when is coalescing as fast as meeting"):
  with members:
    - "ignore or comply on breaking symmetry in consensus"
    - "bounds on the voter model in dynamic networks"
    - "stabilizing consensus with many opinions"
    - "the power of two choices in distributed voting"
    - "plurality consensus in the gossip model"
    - "coalescing random walks and voting on connected graphs"

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.DM(Discrete Mathematics)