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: "an empirical study on the practical impact of prior beliefs over policy types"
  Abstract: Many multiagent applications require an agent to learn quickly how to interact with previously unknown other agents. To address this problem, researchers have studied learning algorithms which compute posterior beliefs over a hypothesised set of policies, based on the observed actions of the other agents. The posterior belief is complemented by the prior belief, which specifies the subjective likelihood of policies before any actions are observed. In this paper, we present the first comprehensive empirical study on the practical impact of prior beliefs over policies in repeated interactions. We show that prior beliefs can have a significant impact on the long-term performance of such methods, and that the magnitude of the impact depends on the depth of the planning horizon. Moreover, our results demonstrate that automatic methods can be used to compute prior beliefs with consistent performance effects. This indicates that prior beliefs could be eliminated as a manual parameter and instead be computed automatically.

Connected through hyperedge HE-1 (induced by source paper "are you doing what i think you are doing criticising uncertain agent models"):
  with members:
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "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-2 (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"
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "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-3 (induced by source paper "belief and truth in hypothesised behaviours"):
  with members:
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "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)