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: "monte carlo tree search for simulation based strategy analysis"
  Abstract: Games are often designed to shape player behavior in a desired way; however, it can be unclear how design decisions affect the space of behaviors in a game. Designers usually explore this space through human playtesting, which can be time-consuming and of limited effectiveness in exhausting the space of possible behaviors. In this paper, we propose the use of automated planning agents to simulate humans of varying skill levels to generate game playthroughs. Metrics can then be gathered from these playthroughs to evaluate the current game design and identify its potential flaws. We demonstrate this technique in two games: the popular word game Scrabble and a collectible card game of our own design named Cardonomicon. Using these case studies, we show how using simulated agents to model humans of varying skill levels allows us to extract metrics to describe game balance (in the case of Scrabble) and highlight potential design flaws (in the case of Cardonomicon).

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"
    - "world models"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "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"
    - "backplay man muss immer umkehren"
    - "deep multi agent reinforcement learning with relevance graphs"
    - "pommerman a multi agent playground"

Connected through hyperedge HE-3 (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"
    - "planning with a receding horizon for manipulation in clutter using a learned value function"
    - "pommerman a multi agent playground"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "deep reinforcement learning a brief survey"
    - "deep reinforcement learning an overview"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-4 (induced by source paper "mapping hearthstone deck spaces through map elites with sliding boundaries"):
  with members:
    - "talakat bullet hell generation through constrained map elites"
    - "q deckrec a fast deck recommendation system for collectible card games"
    - "improving hearthstone ai by combining mcts and supervised learning algorithms"
    - "helping ai to play hearthstone aaia 17 data mining challenge"
    - "demonstrating the feasibility of automatic game balancing"
    - "robots that can adapt like animals"
    - "evolving a behavioral repertoire for a walking robot"

Connected through hyperedge HE-5 (induced by source paper "taking the scenic route automatic exploration for videogames"):
  with members:
    - "playing hard exploration games by watching youtube"
    - "large scale study of curiosity driven learning"
    - "planning with pixels in almost real time"
    - "automatic mapping of nes games with mappy"
    - "automated game design learning"
    - "unifying count based exploration and intrinsic motivation"
    - "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)