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 "a graphical evolutionary game approach to social learning"):
  Abstract: In this work, we study the social learning problem, in which agents of a networked system collaborate to detect the state of the nature based on their private signals. A novel distributed graphical evolutionary game-theoretic learning method is proposed. In the proposed game-theoretic method, agents only need to communicate their binary decisions rather than the real-valued beliefs with their neighbors, which endows the method with low communication complexity. Under mean field approximations, we theoretically analyze the steady-state equilibria of the game and show that the evolutionarily stable states coincide with the decisions of the benchmark centralized detector. Numerical experiments are implemented to confirm the effectiveness of the proposed game-theoretic learning method.

Includes members:
  Member "bayesian quadratic network game filters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning decentralized controllers for robot swarms with graph neural networks")
  Member "graphical evolutionary game for information diffusion over social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "modeling of spatio temporal hawkes processes with randomized kernels")
  Member "distributed adaptive networks a graphical evolutionary game theoretic view":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "game theory for signal processing in networks")
  Member "hypothesis testing in feedforward networks with broadcast failures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "social learning with questions")
  Member "on learning with finite memory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "social learning with questions")
  Member "gossip algorithms for distributed signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inverse graph learning over optimization 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.GT(Computer Science and Game Theory)