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: "on convergence and optimality of best response learning with policy types in multiagent systems"
  Abstract: While many multiagent algorithms are designed for homogeneous systems (i.e. all agents are identical), there are important applications which require an agent to coordinate its actions without knowing a priori how the other agents behave. One method to make this problem feasible is to assume that the other agents draw their latent policy (or type) from a specific set, and that a domain expert could provide a specification of this set, albeit only a partially correct one. Algorithms have been proposed by several researchers to compute posterior beliefs over such policy libraries, which can then be used to determine optimal actions. In this paper, we provide theoretical guidance on two central design parameters of this method: Firstly, it is important that the user choose a posterior which can learn the true distribution of latent types, as otherwise suboptimal actions may be chosen. We analyse convergence properties of two existing posterior formulations and propose a new posterior which can learn correlated distributions. Secondly, since the types are provided by an expert, they may be inaccurate in the sense that they do not predict the agents' observed actions. We provide a novel characterisation of optimality which allows experts to use efficient model checking algorithms to verify optimality of types.

Connected through hyperedge HE-1 (induced by source paper "reasoning about hypothetical agent behaviours and their parameters"):
  with members:
    - "belief and truth in hypothesised behaviours"
    - "exploiting causality for selective belief filtering in dynamic bayesian networks"
    - "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems"
    - "team behavior in interactive dynamic influence diagrams with applications to ad hoc teams"
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"
    - "tractable inference for complex stochastic processes"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "bayes bluff opponent modelling in poker"

Connected through hyperedge HE-2 (induced by source paper "are you doing what i think you are doing criticising uncertain agent models"):
  with members:
    - "an empirical study on the practical impact of prior beliefs over policy types"
    - "a framework for sequential planning in multi agent settings"
    - "awesome a general multiagent learning algorithm that converges in self play and learns a best response against stationary opponents"

Connected through hyperedge HE-3 (induced by source paper "an empirical study on the practical impact of prior beliefs over policy types"):
  with members:
    - "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems"
    - "a framework for sequential planning in multi agent settings"

Connected through hyperedge HE-4 (induced by source paper "e hba using action policies for expert advice and agent typification"):
  with members:
    - "online bandit learning against an adaptive adversary from regret to policy regret"

Connected through hyperedge HE-5 (induced by source paper "autonomous agents modelling other agents a comprehensive survey and open problems"):
  with members:
    - "reasoning about hypothetical agent behaviours and their parameters"
    - "are you doing what i think you are doing criticising uncertain agent models"
    - "e hba using action policies for expert advice and agent typification"
    - "an empirical study on the practical impact of prior beliefs over policy types"
    - "heuristic online goal recognition in continuous domains"
    - "a survey of learning in multiagent environments dealing with non stationarity"
    - "belief and truth in hypothesised behaviours"
    - "opponent modeling in deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "learning best response strategies for agents in ad exchanges"):
  with members:
    - "belief and truth in hypothesised behaviours"
    - "the sample complexity of revenue maximization"
    - "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems"
    - "making the most of your samples"
    - "learning prices for repeated auctions with strategic buyers"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"
    - "censored exploration and the dark pool problem"
    - "budget optimization for sponsored search censored learning in mdps"

Connected through hyperedge HE-7 (induced by source paper "learning sharing behaviors with arbitrary numbers of agents"):
  with members:
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "belief and truth in hypothesised behaviours"
    - "team behavior in interactive dynamic influence diagrams with applications to ad hoc teams"
    - "playing atari with deep reinforcement learning"
    - "bayes bluff opponent modelling in poker"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-8 (induced by source paper "belief and truth in hypothesised behaviours"):
  with members:
    - "an empirical study on the practical impact of prior beliefs over policy types"
    - "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems"
    - "model based bayesian exploration"
    - "the complexity of decentralized control of markov decision processes"
    - "bayes bluff opponent modelling in poker"
    - "a framework for sequential planning in multi agent settings"
    - "awesome a general multiagent learning algorithm that converges in self play and learns a best response against stationary opponents"
    - "towards flexible teamwork"

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.AI(Artificial Intelligence)