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: "exploiting structure in cooperative bayesian games"
  Abstract: Cooperative Bayesian games (BGs) can model decision-making problems for teams of agents under imperfect information, but require space and computation time that is exponential in the number of agents. While agent independence has been used to mitigate these problems in perfect information settings, we propose a novel approach for BGs based on the observation that BGs additionally possess a different types of structure, which we call type independence. We propose a factor graph representation that captures both forms of independence and present a theoretical analysis showing that non-serial dynamic programming cannot effectively exploit type independence, while Max-Sum can. Experimental results demonstrate that our approach can tackle cooperative Bayesian games of unprecedented size.

Connected through hyperedge HE-1 (induced by source paper "scalable planning and learning for multiagent pomdps extended version"):
  with members:
    - "learning to cooperate via policy search"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "graphical models for bandit problems"
    - "the divergence of reinforcement learning algorithms with value iteration and function approximation"
    - "online planning algorithms for pomdps"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "a framework for sequential planning in multi agent settings"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"

Connected through hyperedge HE-2 (induced by source paper "exploiting agent and type independence in collaborative graphical bayesian games"):
  with members:
    - "graphical models for game theory"
    - "tractable inference for complex stochastic processes"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "policy iteration for factored mdps"
    - "rollout sampling policy iteration for decentralized pomdps"
    - "map estimation message passing and perfect graphs"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "factored value iteration converges"

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)