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: "distributed community detection in dynamic graphs"
  Abstract: Inspired by the increasing interest in self-organizing social opportunistic networks, we investigate the problem of distributed detection of unknown communities in dynamic random graphs. As a formal framework, we consider the dynamic version of the well-studied Planted Bisection Model    $\mbox{dyn-}\mathcal{G}(n,p,q)$    where the node set [n] of the network is partitioned into two unknown communities and, at every time step, each possible edge (u,v) is active with probability p if both nodes belong to the same community, while it is active with probability q (with q < < p) otherwise. We also consider a time-Markovian generalization of this model.#R##N##R##N#We propose a distributed protocol based on the popular Label Propagation Algorithm and prove that, when the ratio p/q is larger than n b (for an arbitrarily small constant b > 0), the protocol finds the right "planted" partition in O(logn) time even when the snapshots of the dynamic graph are sparse and disconnected (i.e. in the case p = Θ(1/n)).

Connected through hyperedge HE-1 (induced by source paper "efficient distributed community detection in the stochastic block model"):
  with members:
    - "large scale distributed algorithms for facility location with outliers"
    - "local mixing time distributed computation and applications"
    - "near optimal clustering in the k machine model"
    - "average whenever you meet opportunistic protocols for community detection"
    - "on the distributed complexity of large scale graph computations"
    - "find your place simple distributed algorithms for community detection"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "fast distributed algorithms for connectivity and mst in large graphs"

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 "vertex centred method to detect communities in evolving networks"):
  with members:
    - "local community detection by seed expansion from conductance to weighted kernel 1 mean optimization"
    - "fast community structure local uncovering by independent vertex centred process"
    - "overlapping community detection in networks the state of the art and comparative study"

Connected through hyperedge HE-4 (induced by source paper "find your place simple distributed algorithms for community detection"):
  with members:
    - "exact recovery in the stochastic block model"
    - "the relativized second eigenvalue conjecture of alon"
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"
    - "timing in chemical reaction networks"
    - "on the analysis of a label propagation algorithm for community detection"
    - "sufficient conditions for convergence of the sum product algorithm"

Connected through hyperedge HE-5 (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"
    - "simple dynamics for plurality consensus"
    - "timing in chemical reaction networks"
    - "consensus on the initial global majority by local majority polling for a class of sparse graphs"
    - "near optimal random walk sampling in distributed networks"
    - "discovery through gossip"

Connected through hyperedge HE-6 (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"
    - "tight lower bound on the probability of a binomial exceeding its expectation"
    - "consensus on the initial global majority by local majority polling for a class of sparse 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.SI(Social and Information Networks)