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: "improving hearthstone ai by combining mcts and supervised learning algorithms"
  Abstract: We investigate the impact of supervised prediction models on the strength and efficiency of artificial agents that use the Monte-Carlo Tree Search (MCTS) algorithm to play a popular video game Hearthstone: Heroes of Warcraft. We overview our custom implementation of the MCTS that is well-suited for games with partially hidden information and random effects. We also describe experiments which we designed to quantify the performance of our Hearthstone agent's decision making. We show that even simple neural networks can be trained and successfully used for the evaluation of game states. Moreover, we demonstrate that by providing a guidance to the game state search heuristic, it is possible to substantially improve the win rate, and at the same time reduce the required computations.

Connected through hyperedge HE-1 (induced by source paper "covariance matrix adaptation for the rapid illumination of behavior space"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "procedural content generation through quality diversity"
    - "alphastar an evolutionary computation perspective"
    - "evaluating map elites on constrained optimization problems"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "autonomous skill discovery with quality diversity and unsupervised descriptors"
    - "the many ai challenges of hearthstone"
    - "diverse agents for ad hoc cooperation in hanabi"

Connected through hyperedge HE-2 (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"
    - "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"
    - "evaluating and modelling hanabi playing agents"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-3 (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"
    - "atdelfi automatically designing legible full instructions for games"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "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-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"
    - "evaluating and modelling hanabi playing agents"
    - "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 "mapping hearthstone deck spaces through map elites with sliding boundaries"):
  with members:
    - "monte carlo tree search for simulation based strategy analysis"
    - "talakat bullet hell generation through constrained map elites"
    - "q deckrec a fast deck recommendation system for collectible card games"
    - "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-6 (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"
    - "exploring gameplay with ai agents"
    - "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)