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: "deceptive games"
  Abstract: Deceptive games are games where the reward structure or other aspects of the game are designed to lead the agent away from a globally optimal policy. While many games are already deceptive to some extent, we designed a series of games in the Video Game Description Language (VGDL) implementing specific types of deception, classified by the cognitive biases they exploit. VGDL games can be run in the General Video Game Artificial Intelligence (GVGAI) Framework, making it possible to test a variety of existing AI agents that have been submitted to the GVGAI Competition on these deceptive games. Our results show that all tested agents are vulnerable to several kinds of deception, but that different agents have different weaknesses. This suggests that we can use deception to understand the capabilities of a game-playing algorithm, and game-playing algorithms to characterize the deception displayed by a game.

Connected through hyperedge HE-1 (induced by source paper "procedural content generation through quality diversity"):
  with members:
    - "general video game rule generation"
    - "quality diversity through surprise"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "intentional computational level design"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"
    - "adaptive music composition for games"
    - "talakat bullet hell generation through constrained map elites"

Connected through hyperedge HE-2 (induced by source paper "ludii as a competition platform"):
  with members:
    - "general video game rule generation"
    - "an empirical evaluation of two general game systems ludii and rbg"
    - "prolonets neural encoding human experts domain knowledge to warm start reinforcement learning"
    - "ludii the ludemic general game system"
    - "an overview of the ludii general game system"
    - "the 2017 aibirds competition"
    - "atdelfi automatically designing legible full instructions for games"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"

Connected through hyperedge HE-3 (induced by source paper "superstition in the network deep reinforcement learning plays deceptive games"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "deep reinforcement learning for general video game ai"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "deep reinforcement learning a brief survey"
    - "deep learning for video game playing"
    - "asynchronous methods for deep reinforcement learning"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-4 (induced by source paper "ensemble decision systems for general video game playing"):
  with members:
    - "a continuous information gain measure to find the most discriminatory problems for ai benchmarking"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "analysis of vanilla rolling horizon evolution parameters in general video game playing"
    - "population seeding techniques for rolling horizon evolution in general video game playing"

Connected through hyperedge HE-5 (induced by source paper "a continuous information gain measure to find the most discriminatory problems for ai benchmarking"):
  with members:
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "the arcade learning environment an evaluation platform for general agents"

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)