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: "efficient evolutionary methods for game agent optimisation model based is best"
  Abstract: This paper introduces a simple and fast variant of Planet Wars as a test-bed for statistical planning based Game AI agents, and for noisy hyper-parameter optimisation. Planet Wars is a real-time strategy game with simple rules but complex game-play. The variant introduced in this paper is designed for speed to enable efficient experimentation, and also for a fixed action space to enable practical inter-operability with General Video Game AI agents. If we treat the game as a win-loss game (which is standard), then this leads to challenging noisy optimisation problems both in tuning agents to play the game, and in tuning game parameters. Here we focus on the problem of tuning an agent, and report results using the recently developed N-Tuple Bandit Evolutionary Algorithm and a number of other optimisers, including Sequential Model-based Algorithm Configuration (SMAC). Results indicate that the N-Tuple Bandit Evolutionary offers competitive performance as well as insight into the effects of combinations of parameter choices.

Connected through hyperedge HE-1 (induced by source paper "project thyia a forever gameplayer"):
  with members:
    - "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"
    - "don t forget there is more than forgetting new metrics for continual learning"

Connected through hyperedge HE-2 (induced by source paper "a local approach to forward model learning results on the game of life game"):
  with members:
    - "the n tuple bandit evolutionary algorithm for game agent optimisation"
    - "learning and querying fast generative models for reinforcement learning"
    - "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"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "recurrent environment simulators"
    - "action conditional video prediction using deep networks in atari games"
    - "recurrent models of visual attention"

Connected through hyperedge HE-3 (induced by source paper "rinascimento optimising statistical forward planning agents for playing splendor"):
  with members:
    - "the multi agent reinforcement learning in malmo marlo competition"
    - "the n tuple bandit evolutionary algorithm for game agent optimisation"
    - "generative design in minecraft gdmc settlement generation competition"
    - "population seeding techniques for rolling horizon evolution in general video game playing"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "foundations of digital archaeoludology"):
  with members:
    - "biasing mcts with features for general games"
    - "learning policies from self play with policy gradients and mcts value estimates"
    - "thinking fast and slow with deep learning and tree search"

Connected through hyperedge HE-5 (induced by source paper "learning local forward models on unforgiving games"):
  with members:
    - "a local approach to forward model learning results on the game of life game"
    - "the n tuple bandit evolutionary algorithm for game agent optimisation"
    - "imagination augmented agents for 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)