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: "creating pro level ai for real time fighting game with deep reinforcement learning"
  Abstract: Reinforcement learning combined with deep neural networks has performed remarkably well in many genres of game recently. It surpassed human-level performance in fixed game environments and turn-based two player board games. However, no research has ever shown a result that surpassed human level in modern complex fighting games, to the best of our knowledge. This is due to the inherent difficulties of modern fighting games, including vast action spaces, real-time constraints, and performance generalizations required for various opponents. We overcame these challenges and made 1v1 battle AI agents for the commercial game, "Blade & Soul". The trained agents competed against five professional gamers and achieved 62% of win rate.This paper presents a practical reinforcement learning method including a novel self-play curriculum and data skipping techniques. Through the curriculum, three different styles of agents are created by reward shaping, and are trained against each other for robust performance. Additionally, this paper suggests data skipping techniques which increased data efficiency and facilitated explorations in vast spaces.

Connected through hyperedge HE-1 (induced by source paper "deep rl agent for a real time action strategy game"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "high dimensional continuous control using generalized advantage estimation"
    - "playing atari with deep reinforcement learning"

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)