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: "evolving the hearthstone meta"
  Abstract: Balancing an ever growing strategic game of high complexity, such as Hearthstone is a complex task. The target of making strategies diverse and customizable results in a delicate intricate system. Tuning over 2000 cards to generate the desired outcome without disrupting the existing environment becomes a laborious challenge. In this paper, we discuss the impacts that changes to existing cards can have on strategy in Hearthstone. By analyzing the win rate on match-ups across different decks, being played by different strategies, we propose to compare their performance before and after changes are made to improve or worsen different cards. Then, using an evolutionary algorithm, we search for a combination of changes to the card attributes that cause the decks to approach equal, 50% win rates. We then expand our evolutionary algorithm to a multi-objective solution to search for this result, while making the minimum amount of changes, and as a consequence disruption, to the existing cards. Lastly, we propose and evaluate metrics to serve as heuristics with which to decide which cards to target with balance changes.

Connected through hyperedge HE-1 (induced by source paper "evolutionary approach to collectible card game arena deckbuilding using active genes"):
  with members:
    - "the many ai challenges of hearthstone"
    - "q deckrec a fast deck recommendation system for collectible card games"
    - "demonstrating the feasibility of automatic game balancing"

Connected through hyperedge HE-2 (induced by source paper "winning isn t everything enhancing game development with intelligent agents"):
  with members:
    - "on multi agent learning in team sports games"
    - "automated playtesting of matching tile games"
    - "towards interactive training of non player characters in video games"
    - "exploring gameplay with ai agents"
    - "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-3 (induced by source paper "the many ai challenges of hearthstone"):
  with members:
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "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"
    - "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"

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)