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: "general video game ai learning from screen capture"
  Abstract: General Video Game Artificial Intelligence is a general game playing framework for Artificial General Intelligence research in the video-games domain. In this paper, we propose for the first time a screen capture learning agent for General Video Game AI framework. A Deep Q-Network algorithm was applied and improved to develop an agent capable of learning to play different games in the framework. After testing this algorithm using various games of different categories and difficulty levels, the results suggest that our proposed screen capture learning agent has the potential to learn many different games using only a single learning algorithm.

Connected through hyperedge HE-1 (induced by source paper "project thyia a forever gameplayer"):
  with members:
    - "efficient evolutionary methods for game agent optimisation model based is best"
    - "go explore a new approach for hard exploration problems"
    - "continual lifelong learning with neural networks a review"
    - "rinascimento optimising statistical forward planning agents for playing splendor"
    - "a local approach to forward model learning results on the game of life game"
    - "deep reinforcement learning for general video game ai"
    - "the n tuple bandit evolutionary algorithm for game agent optimisation"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-2 (induced by source paper "learning to play general video games via an object embedding network"):
  with members:
    - "investigating human priors for playing video games"
    - "deep sets"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "grounding language for transfer in deep reinforcement learning"
    - "visual interaction networks"
    - "a simple neural network module for relational reasoning"
    - "hybrid reward architecture for reinforcement learning"
    - "neural episodic control"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning for general video game ai"):
  with members:
    - "general video game rule generation"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "deep learning in neural networks"
    - "dueling network architectures for deep reinforcement learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "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)