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: "deviator detection under imperfect monitoring"
  Abstract: Grim-trigger strategies are a fundamental mechanism for sustaining equilibria in iterated games: the players cooperate along an agreed path, and as soon as one player deviates, the others form a coalition to play him down to his minmax level. A precondition to triggering such a strategy is that the identity of the deviating player becomes common knowledge among the other players. This can be difficult or impossible to attain in games where the information structure allows only imperfect monitoring of the played actions or of the global state. #R##N#We study the problem of synthesising finite-state strategies for detecting the deviator from an agreed strategy profile in games played on finite graphs with different information structures. We show that the problem is undecidable in the general case where the global state cannot be monitored. On the other hand, we prove that under perfect monitoring of the global state and imperfect monitoring of actions, the problem becomes decidable, and we present an effective synthesis procedure that covers infinitely repeated games with private monitoring.

Connected through hyperedge HE-1 (induced by source paper "games on graphs with a public signal monitoring"):
  with members:
    - "decidability results for atl with imperfect information and perfect recall"
    - "strategy logic with imperfect information"
    - "hierarchical information and the synthesis of distributed strategies"
    - "infinite games with finite knowledge gaps"
    - "the complexity of nash equilibria in limit average games"
    - "model checking an alternating time temporal logic with knowledge imperfect information perfect recall and communicating coalitions"
    - "algorithms for omega regular games with imperfect information"

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)