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: "exploring gameplay with ai agents"
  Abstract: The process of playtesting a game is subjective, expensive and incomplete. In this paper, we present a playtesting approach that explores the game space with automated agents and collects data to answer questions posed by the designers. Rather than have agents interacting with an actual game client, this approach recreates the bare bone mechanics of the game as a separate system. Our agent is able to play in minutes what would take testers days of organic gameplay. The analysis of thousands of game simulations exposed imbalances in game actions, identified inconsequential rewards and evaluated the effectiveness of optional strategic choices. Our test case game, The Sims Mobile, was recently released and the findings shown here influenced design changes that resulted in improved player experience.

Connected through hyperedge HE-1 (induced by source paper "winning isn t everything enhancing game development with intelligent agents"):
  with members:
    - "on multi agent learning in team sports games"
    - "evolving the hearthstone meta"
    - "automated playtesting of matching tile games"
    - "towards interactive training of non player characters in video games"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "playing atari with six neurons"
    - "rainbow combining improvements in deep reinforcement learning"
    - "starcraft ii a new challenge for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "winning isn t everything training human like agents for playtesting and game ai"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "on reinforcement learning for full length game of starcraft"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "rainbow combining improvements in deep reinforcement learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "procedural content generation via machine learning pcgml"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-3 (induced by source paper "evolving the hearthstone meta"):
  with members:
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "q deckrec a fast deck recommendation system for collectible card games"
    - "improving hearthstone ai by combining mcts and supervised learning algorithms"
    - "demonstrating the feasibility of automatic game balancing"
    - "illuminating search spaces by mapping elites"

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)