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: "consensus on the initial global majority by local majority polling for a class of sparse graphs"
  Abstract: We study the local majority protocol on simple graphs of a given degree sequence, for a certain class of degree sequences. We show that for almost all such graphs, subject to a sufficiently large bias, within time $A \log_d \log_d n$ the local majority protocol achieves consensus on the initial global majority with probability $1-n^{-\Omega((\log n)^{\varepsilon})}$, where $\varepsilon>0$ is a constant. $A$ is bounded by a universal constant and $d$ is a parameter of the graph; the smallest integer which is the degree of $\Theta(n)$ vertices in the graph. We further show that under the assumption that a vertex $v$ does not change its colour if it and all of its neighbours are the same colour, \emph{any} local protocol $\mathcal{P}$ takes time at least $(1-o(1))\log_d\log_d n$, with probability $1-e^{-\Omega(n^{1-o(1)})}$ on such graphs. We further show that for almost all $d$-regular simple graphs with $d$ constant, we can get a stronger probability to convergence of initial majority at the expense of time. Specifically, with probability $1-O\brac{c^{-n^{\varepsilon}}}$, the local majority protocol achieves consensus on the initial majority by time $O(\log n)$. Finally, we show how the technique for the above sparse graphs can be applied in a straightforward manner to get bounds for the Erd\H{o}s--Renyi random graphs in the connected regime.

Connected through hyperedge HE-1 (induced by source paper "on the parallel undecided state dynamics with two colors"):
  with members:
    - "ignore or comply on breaking symmetry in consensus"
    - "tight analysis for the 3 majority consensus dynamics"
    - "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"
    - "determining majority in networks with local interactions and very small local memory"
    - "simple dynamics for plurality consensus"

Connected through hyperedge HE-2 (induced by source paper "plurality consensus in the gossip model"):
  with members:
    - "the power of two choices in distributed voting"
    - "determining majority in networks with local interactions and very small local memory"
    - "rumor spreading in random evolving graphs"
    - "distributed community detection in dynamic graphs"
    - "simple dynamics for plurality consensus"
    - "timing in chemical reaction networks"
    - "near optimal random walk sampling in distributed networks"
    - "discovery through gossip"

Connected through hyperedge HE-3 (induced by source paper "the power of two choices in distributed voting"):
  with members:
    - "simple dynamics for plurality consensus"
    - "coalescing random walks and voting on connected graphs"

Connected through hyperedge HE-4 (induced by source paper "simple dynamics for plurality consensus"):
  with members:
    - "global majority consensus by local majority polling on graphs of a given degree sequence"
    - "plurality consensus in the gossip model"
    - "determining majority in networks with local interactions and very small local memory"
    - "distributed community detection in dynamic graphs"
    - "tight lower bound on the probability of a binomial exceeding its expectation"

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)