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: "automated playtesting with procedural personas through mcts with evolved heuristics"
  Abstract: This paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas. Theoretically grounded in psychological decision theory, procedural personas are implemented using a variation of Monte Carlo Tree Search (MCTS) where the node selection criteria are developed using evolutionary computation, replacing the standard UCB1 criterion of MCTS. Using these personas we demonstrate how generative player models can be applied to a varied corpus of game levels and demonstrate how different play styles can be enacted in each level. In short, we use artificially intelligent personas to construct synthetic playtesters. The proposed approach could be used as a tool for automatic play testing when human feedback is not readily available or when quick visualization of potential interactions is necessary. Possible applications include interactive tools during game development or procedural content generation systems where many evaluations must be conducted within a short time span.

Connected through hyperedge HE-1 (induced by source paper "conceptual game expansion"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "automatic game design via mechanic generation"
    - "monte carlo tree search for simulation based strategy analysis"
    - "world models"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "automated game design via conceptual expansion"
    - "automatic mapping of nes games with mappy"
    - "procedural content generation via machine learning pcgml"

Connected through hyperedge HE-2 (induced by source paper "safer deep rl with shallow mcts a case study in pommerman"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "real time tree search with pessimistic scenarios"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "wisemove a framework for safe deep reinforcement learning for autonomous driving"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "monte carlo tree search for simulation based strategy analysis"
    - "backplay man muss immer umkehren"
    - "deep multi agent reinforcement learning with relevance graphs"

Connected through hyperedge HE-3 (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"
    - "exploring gameplay with ai agents"
    - "playing atari with six neurons"
    - "rainbow combining improvements in deep reinforcement learning"
    - "starcraft ii a new challenge for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "bootstrapping conditional gans for video game level generation"):
  with members:
    - "general video game rule generation"
    - "doom level generation using generative adversarial networks"
    - "image transformer"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "atdelfi automatically designing legible full instructions for games"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "on convergence and stability of gans"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-5 (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"
    - "exploring gameplay with ai agents"
    - "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-6 (induced by source paper "action guidance with mcts for deep reinforcement learning"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "wisemove a framework for safe deep reinforcement learning for autonomous driving"
    - "monte carlo tree search for simulation based strategy analysis"
    - "planning with a receding horizon for manipulation in clutter using a learned value function"
    - "pommerman a multi agent playground"
    - "deep reinforcement learning a brief survey"
    - "deep reinforcement learning an overview"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-7 (induced by source paper "the many ai challenges of hearthstone"):
  with members:
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "evolving the hearthstone meta"
    - "co creative level design via machine learning"
    - "improving hearthstone ai by combining mcts and supervised learning algorithms"
    - "atdelfi automatically designing legible full instructions for games"
    - "helping ai to play hearthstone aaia 17 data mining challenge"
    - "latent predictor networks for code generation"
    - "robots that can adapt like animals"

Connected through hyperedge HE-8 (induced by source paper "automatic critical mechanic discovery in video games"):
  with members:
    - "general video game rule generation"
    - "intentional computational level design"
    - "deep reinforcement learning for general video game ai"
    - "press space to fire automatic video game tutorial generation"
    - "atdelfi automatically designing legible full instructions for games"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "generating levels that teach mechanics"
    - "deriving quests from open world mechanics"

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)