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 "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)).

Includes members:
  Member "on the analysis of a label propagation algorithm for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient distributed community detection in the stochastic block model")
  Member "information spreading in stationary markovian evolving graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how fast can we reach a target vertex in stochastic temporal graphs")
  Member "performance of opportunistic epidemic routing on edge markovian dynamic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the performance analysis of epidemic routing in non sparse delay tolerant networks")

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.SI(Social and Information Networks)