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: "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.

Connected through hyperedge HE-1 (induced by source paper "integrating planning and interpretable goal recognition for autonomous driving"):
  with members:
    - "when humans aren t optimal robots that collaborate with risk aware humans"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "learning trajectory prediction with continuous inverse optimal control via langevin sampling of energy based models"
    - "energy based continuous inverse optimal control"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "efficient planning under uncertainty with macro actions"

Connected through hyperedge HE-2 (induced by source paper "reasoning about hypothetical agent behaviours and their parameters"):
  with members:
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "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-3 (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"
    - "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"
    - "opponent modeling in deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "teaching social behavior through human reinforcement for ad hoc teamwork the star framework"):
  with members:
    - "reasoning about hypothetical agent behaviours and their parameters"
    - "incorporating behavioral constraints in online ai systems"
    - "building ethically bounded ai"
    - "incomplete contracting and ai alignment"
    - "building ethics into artificial intelligence"
    - "inverse reward design"
    - "safe reinforcement learning via shielding"
    - "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems"

Connected through hyperedge HE-5 (induced by source paper "learning best response strategies for agents in ad exchanges"):
  with members:
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "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-6 (induced by source paper "learning sharing behaviors with arbitrary numbers of agents"):
  with members:
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "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-7 (induced by source paper "courtesy as a means to coordinate"):
  with members:
    - "anytime heuristic for weighted matching through altruism inspired behavior"
    - "decentralized anti coordination through multi agent learning"
    - "contextual bandit algorithms with supervised learning guarantees"

Connected through hyperedge HE-8 (induced by source paper "a survey of learning in multiagent environments dealing with non stationarity"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"
    - "opponent modeling in deep reinforcement learning"
    - "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems"
    - "individual planning in agent populations exploiting anonymity and frame action hypergraphs"

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)