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: "decentralized bayesian learning in dynamic games"
  Abstract: We study the problem of decentralized Bayesian learning in a dynamical system involving strategic agents with asymmetric information. In a series of seminal papers in the literature, this problem has been studied under a simplifying model where selfish players appear sequentially and act once in the game, based on private noisy observations of the system state and public observation of past players' actions. It is shown that there exist information cascades where users discard their private information and mimic the action of their predecessor. In this paper, we provide a framework for studying Bayesian learning dynamics in a more general setting than the one described above. In particular, our model incorporates cases where players participate for the whole duration of the game, and cases where an endogenous process selects which subset of players will act at each time instance. The proposed methodology hinges on a sequential decomposition for finding perfect Bayesian equilibria (PBE) of a general class of dynamic games with asymmetric information, where user-specific states evolve as conditionally independent Markov process and users make independent noisy observations of their states. Using our methodology, we study a specific dynamic learning model where players make decisions about investing in the team, based on their estimates of everyone's types. We characterize a set of informational cascades for this problem where learning stops for the team as a whole.

Connected through hyperedge HE-1 (induced by source paper "signaling equilibria in mean field games"):
  with members:
    - "dynamic games with asymmetric information common information based perfect bayesian equilibria and sequential decomposition"
    - "decentralized stochastic control with partial history sharing a common information approach"
    - "equilibria of dynamic games with many players existence approximation and market structure"

Connected through hyperedge HE-2 (induced by source paper "markov perfect equilibria in non stationary mean field games"):
  with members:
    - "dynamic games with asymmetric information common information based perfect bayesian equilibria and sequential decomposition"
    - "decentralized stochastic control with partial history sharing a common information approach"
    - "equilibria of dynamic games with many players existence approximation and market structure"

Connected through hyperedge HE-3 (induced by source paper "sequential decomposition of dynamic games with asymmetric information and dependent states"):
  with members:
    - "safe and nested subgame solving for imperfect information games"
    - "dynamic games with asymmetric information common information based perfect bayesian equilibria and sequential decomposition"
    - "decentralized stochastic control with partial history sharing a common information approach"
    - "optimal decentralized control of coupled subsystems with control sharing"

Connected through hyperedge HE-4 (induced by source paper "structured perfect bayesian equilibrium in infinite horizon dynamic games with asymmetric information"):
  with members:
    - "signaling equilibria for dynamic lqg games with asymmetric information"
    - "common information based markov perfect equilibria for linear gaussian games with asymmetric information"
    - "decentralized stochastic control with partial history sharing a common information approach"

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.GT(Computer Science and Game Theory)