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 "infection spreading and source identification a hide and seek game"):
  Abstract: The goal of an infection source node (e.g., a rumor or computer virus source) in a network is to spread its infection to as many nodes as possible, while remaining hidden from the network administrator. On the other hand, the network administrator aims to identify the source node based on knowledge of which nodes have been infected. We model the infection spreading and source identification problem as a strategic game, where the infection source and the network administrator are the two players. As the Jordan center estimator is a minimax source estimator that has been shown to be robust in recent works, we assume that the network administrator utilizes a source estimation strategy that can probe any nodes within a given radius of the Jordan center. Given any estimation strategy, we design a best-response infection strategy for the source. Given any infection strategy, we design a best-response estimation strategy for the network administrator. We derive conditions under which a Nash equilibrium of the strategic game exists. Simulations in both synthetic and real-world networks demonstrate that our proposed infection strategy infects more nodes while maintaining the same safety margin between the true source node and the Jordan center source estimator.

Includes members:
  Member "on the universality of jordan centers for estimating infection sources in tree networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gfcn a new graph convolutional network based on parallel flows")
  Member "robust decentralized detection and social learning in tandem networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an unsupervised bayesian neural network for truth discovery")
  Member "whose opinion to follow in multihypothesis social learning a large deviations perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an unsupervised bayesian neural network for truth discovery")
  Member "how to identify an infection source with limited observations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate identification of the optimal epidemic source in complex networks")
  Member "spy vs spy rumor source obfuscation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning mixtures of graphs from epidemic cascades")
  Member "graphical evolutionary game for information diffusion over social networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "co contagion diffusion on multilayer networks")
  Member "evolutionary dynamics of information diffusion over social networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "modeling of spatio temporal hawkes processes with randomized kernels")
  Member "identifying infection sources and regions in large networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gfcn a new graph convolutional network based on parallel flows")

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)