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 "belief and truth in hypothesised behaviours"):
  Abstract: There is a long history in game theory on the topic of Bayesian or "rational" learning, in which each player maintains beliefs over a set of alternative behaviours, or types, for the other players. This idea has gained increasing interest in the artificial intelligence (AI) community, where it is used as a method to control a single agent in a system composed of multiple agents with unknown behaviours. The idea is to hypothesise a set of types, each specifying a possible behaviour for the other agents, and to plan our own actions with respect to those types which we believe are most likely, given the observed actions of the agents. The game theory literature studies this idea primarily in the context of equilibrium attainment. In contrast, many AI applications have a focus on task completion and payoff maximisation. With this perspective in mind, we identify and address a spectrum of questions pertaining to belief and truth in hypothesised types. We formulate three basic ways to incorporate evidence into posterior beliefs and show when the resulting beliefs are correct, and when they may fail to be correct. Moreover, we demonstrate that prior beliefs can have a significant impact on our ability to maximise payoffs in the long-term, and that they can be computed automatically with consistent performance effects. Furthermore, we analyse the conditions under which we are able complete our task optimally, despite inaccuracies in the hypothesised types. Finally, we show how the correctness of hypothesised types can be ascertained during the interaction via an automated statistical analysis.

Includes members:
  Member "on convergence and optimality of best response learning with policy types in multiagent systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reasoning about hypothetical agent behaviours and their parameters")
  Member "an empirical study on the practical impact of prior beliefs over policy types":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "are you doing what i think you are doing criticising uncertain agent models")
  Member "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "autonomous industrial management via reinforcement learning self learning agents for decision making a review")
  Member "model based bayesian exploration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "benchmarking model based reinforcement learning")
  Member "the complexity of decentralized control of markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "human level performance in 3d multiplayer games with population based reinforcement learning")
  Member "bayes bluff opponent modelling in poker":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "a framework for sequential planning in multi agent settings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "awesome a general multiagent learning algorithm that converges in self play and learns a best response against stationary opponents":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a theoretical analysis of deep q learning")

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)