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 "exploiting agent and type independence in collaborative graphical bayesian games"):
  Abstract: Efficient collaborative decision making is an important cha llenge for multiagent systems. Finding optimal joint actions is especially challenging when each agent has only imperfect information about the state of its environment. Such problems can be modeled as collaborative Bayesian gamesin which each agent receives private information in the form of its type. However, representing and solving such games requires space and computation time exponential in the number of agents. This article introduces collaborative graphical Bayesian games (CGBGs) , which facilitate more efficient collaborative decision ma king by decomposing the global payoff function as the sum of local payoff functions that depend on only a few agents. We propose a framework for the efficient solution of CGBGs based on the ins ight that they posses two different types of independence, which we call agent independence and type independence. In particular, we present a factor graph representation that captures both forms of independence and thus enables efficient solutions. In addition, we show how this representation can provide leverage in sequential tasks by using it to construct a novel method for decentralized partially observable Markov decision proce sses. Experimental results in both random and benchmark tasks demonstrate the improved scalability of our methods compared to several existing alternatives.

Includes members:
  Member "graphical models for game theory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "tractable inference for complex stochastic processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explaining deep neural networks with a polynomial time algorithm for shapley values approximation")
  Member "the factored frontier algorithm for approximate inference in dbns":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reasoning about hypothetical agent behaviours and their parameters")
  Member "policy iteration for factored mdps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi agent thompson sampling for bandit applications with sparse neighbourhood structures")
  Member "exploiting structure in cooperative bayesian games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable planning and learning for multiagent pomdps extended version")
  Member "rollout sampling policy iteration for decentralized pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep decentralized multi task multi agent reinforcement learning under partial observability")
  Member "map estimation message passing and perfect graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hinge loss markov random fields and probabilistic soft logic")
  Member "optimal and approximate q value functions for decentralized pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep multi agent reinforcement learning for decentralized continuous cooperative control")

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.AI(Artificial Intelligence)