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: "evaluating and modelling hanabi playing agents"
  Abstract: Agent modelling involves considering how other agents will behave, in order to influence your own actions. In this paper, we explore the use of agent modelling in the hidden-information, collaborative card game Hanabi. We implement a number of rule-based agents, both from the literature and of our own devising, in addition to an Information Set Monte Carlo Tree Search (IS-MCTS) agent. We observe poor results from IS-MCTS, so construct a new, predictor version that uses a model of the agents with which it is paired. We observe a significant improvement in game-playing strength from this agent in comparison to IS-MCTS, resulting from its consideration of what the other agents in a game would do. In addition, we create a flawed rule-based agent to highlight the predictor's capabilities with such an agent.

Connected through hyperedge HE-1 (induced by source paper "the hanabi challenge a new frontier for ai research"):
  with members:
    - "re determinizing information set monte carlo tree search in hanabi"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "evolving agents for the hanabi 2018 cig competition"

Connected through hyperedge HE-2 (induced by source paper "improving policies via search in cooperative partially observable games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "rethinking formal models of partially observable multiagent decision making"
    - "simplified action decoder for deep multi agent reinforcement learning"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "distributed prioritized experience replay"
    - "prioritized experience replay"

Connected through hyperedge HE-3 (induced by source paper "survey of artificial intelligence for card games and its application to the swiss game jass"):
  with members:
    - "improving search with supervised learning in trick based card games"
    - "solving imperfect information games via discounted regret minimization"
    - "solving large sequential games with the excessive gap technique"
    - "deep counterfactual regret minimization"
    - "improving hearthstone ai by combining mcts and supervised learning algorithms"
    - "application of self play reinforcement learning to a four player game of imperfect information"
    - "traditional wisdom and monte carlo tree search face to face in the card game scopone"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-4 (induced by source paper "re determinizing information set monte carlo tree search in hanabi"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "evolving agents for the hanabi 2018 cig competition"
    - "improving hearthstone ai by combining mcts and supervised learning algorithms"
    - "combining prediction of human decisions with ismcts in imperfect information games"
    - "thinking fast and slow with deep learning and tree search"
    - "hanabi is np hard even for cheaters who look at their cards"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-5 (induced by source paper "diverse agents for ad hoc cooperation in hanabi"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "re determinizing information set monte carlo tree search in hanabi"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "re evaluating evaluation"
    - "evolving agents for the hanabi 2018 cig competition"
    - "towards game based metrics for computational co creativity"
    - "robots that can adapt like animals"

Connected through hyperedge HE-6 (induced by source paper "evolving agents for the hanabi 2018 cig competition"):
  with members:
    - "hanabi is np complete even for cheaters who look at their cards"
    - "playing atari with deep reinforcement learning"

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)