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: "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems"
  Abstract: The ad hoc coordination problem is to design an autonomous agent which is able to achieve optimal flexibility and efficiency in a multiagent system with no mechanisms for prior coordination. We conceptualise this problem formally using a game-theoretic model, called the stochastic Bayesian game, in which the behaviour of a player is determined by its private information, or type. Based on this model, we derive a solution, called Harsanyi-Bellman Ad Hoc Coordination (HBA), which utilises the concept of Bayesian Nash equilibrium in a planning procedure to find optimal actions in the sense of Bellman optimal control. We evaluate HBA in a multiagent logistics domain called level-based foraging, showing that it achieves higher flexibility and efficiency than several alternative algorithms. We also report on a human-machine experiment at a public science exhibition in which the human participants played repeated Prisoner's Dilemma and Rock-Paper-Scissors against HBA and alternative algorithms, showing that HBA achieves equal efficiency and a significantly higher welfare and winning rate.

Connected through hyperedge HE-1 (induced by source paper "autonomous industrial management via reinforcement learning self learning agents for decision making a review"):
  with members:
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "world models"
    - "a web page classifier library based on random image content analysis using deep learning"
    - "imitation learning with concurrent actions in 3d games"
    - "one shot visual imitation learning via meta learning"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "deep reinforcement learning a brief survey"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

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"
    - "belief and truth in hypothesised behaviours"
    - "exploiting causality for selective belief filtering in dynamic bayesian networks"
    - "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 "an empirical study on the practical impact of prior beliefs over policy types"):
  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"

Connected through hyperedge HE-4 (induced by source paper "on convergence and optimality of best response learning with policy types in multiagent systems"):
  with members:
    - "a framework for sequential planning in multi agent settings"

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"
    - "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"
    - "belief and truth in hypothesised behaviours"

Connected through hyperedge HE-6 (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"
    - "belief and truth in hypothesised behaviours"

Connected through hyperedge HE-7 (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"
    - "belief and truth in hypothesised behaviours"
    - "the sample complexity of revenue maximization"
    - "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-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"
    - "belief and truth in hypothesised behaviours"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"
    - "opponent modeling in deep reinforcement learning"
    - "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.GT(Computer Science and Game Theory)