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 "phase transition of the 2 choices dynamics on core periphery networks"):
  Abstract: Consider the following process on a network: Each agent initially holds either opinion blue or red; then, in each round, each agent looks at two random neighbors and, if the two have the same opinion, the agent adopts it. This process is known as the 2-Choices dynamics and is arguably the most basic non-trivial opinion dynamics modeling voting behavior on social networks. Despite its apparent simplicity, 2-Choices has been analytically characterized only on networks with a strong expansion property -- under assumptions on the initial configuration that establish it as a fast majority consensus protocol. #R##N#In this work, we aim at contributing to the understanding of the 2-Choices dynamics by considering its behavior on a class of networks with core-periphery structure, a well-known topological assumption in social networks. In a nutshell, assume that a densely-connected subset of agents, the core, holds a different opinion from the rest of the network, the periphery. Then, depending on the strength of the cut between the core and the periphery, a phase-transition phenomenon occurs: Either the core's opinion rapidly spreads among the rest of the network, or a metastability phase takes place, in which both opinions coexist in the network for superpolynomial time. The interest of our result is twofold. On the one hand, by looking at the 2-Choices dynamics as a simplistic model of competition among opinions in social networks, our theorem sheds light on the influence of the core on the rest of the network, as a function of the core's connectivity towards the latter. On the other hand, to the best of our knowledge, we provide the first analytical result which shows a heterogeneous behavior of a simple dynamics as a function of structural parameters of the network. Finally, we validate our theoretical predictions with extensive experiments on real networks.

Includes members:
  Member "tight analysis for the 3 majority consensus dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the necessary memory to compute the plurality in multi agent systems")
  Member "the coalescing branching random walk on expanders and the dual epidemic process":
    also connected through other hyperedges:
      (none)
  Member "bounds on the voter model in dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics")
  Member "fast plurality consensus in regular expanders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "best of three voting on dense graphs")
  Member "discordant voting processes on finite graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication cost of consensus for nodes with limited memory")
  Member "stabilizing consensus with many opinions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the necessary memory to compute the plurality in multi agent systems")
  Member "core periphery in networks an axiomatic approach":
    also connected through other hyperedges:
      (none)
  Member "the power of two choices in distributed voting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed voting in beep model")

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)