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: "the n tuple bandit evolutionary algorithm for automatic game improvement"
  Abstract: This paper describes a new evolutionary algorithm that is especially well suited to AI-Assisted Game Design. The approach adopted in this paper is to use observations of AI agents playing the game to estimate the game's quality. Some of best agents for this purpose are General Video Game AI agents, since they can be deployed directly on a new game without game-specific tuning; these agents tend to be based on stochastic algorithms which give robust but noisy results and tend to be expensive to run. This motivates the main contribution of the paper: the development of the novel N-Tuple Bandit Evolutionary Algorithm, where a model is used to estimate the fitness of unsampled points and a bandit approach is used to balance exploration and exploitation of the search space. Initial results on optimising a Space Battle game variant suggest that the algorithm offers far more robust results than the Random Mutation Hill Climber and a Biased Mutation variant, which are themselves known to offer competitive performance across a range of problems. Subjective observations are also given by human players on the nature of the evolved games, which indicate a preference towards games generated by the N-Tuple 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 "efficient evolutionary methods for game agent optimisation model based is best"):
  with members:
    - "the n tuple bandit evolutionary algorithm for game agent optimisation"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "game ai research with fast planet wars variants"
    - "bandit based random mutation hill climbing"

Connected through hyperedge HE-3 (induced by source paper "the n tuple bandit evolutionary algorithm for game agent optimisation"):
  with members:
    - "analysis of vanilla rolling horizon evolution parameters in general video game playing"
    - "population seeding techniques for rolling horizon evolution in general video game playing"
    - "evolving game skill depth using general video game ai agents"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "population based training of neural networks"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "bandit based random mutation hill climbing"
    - "unifying count based exploration and intrinsic motivation"

Connected through hyperedge HE-4 (induced by source paper "general video game ai a multi track framework for evaluating agents games and content generation algorithms"):
  with members:
    - "general video game rule generation"
    - "press space to fire automatic video game tutorial generation"
    - "a continuous information gain measure to find the most discriminatory problems for ai benchmarking"
    - "learning to play general video games via an object embedding network"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-5 (induced by source paper "game ai research with fast planet wars variants"):
  with members:
    - "the n tuple bandit evolutionary algorithm for game agent optimisation"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"

Connected through hyperedge HE-6 (induced by source paper "evolving game skill depth using general video game ai agents"):
  with members:
    - "bandit based random mutation hill climbing"
    - "optimal resampling for the noisy onemax problem"
    - "rolling horizon coevolutionary planning for two player video games"

Connected through hyperedge HE-7 (induced by source paper "efficient noisy optimisation with the sliding window compact genetic algorithm"):
  with members:
    - "evolving game skill depth using general video game ai agents"

Connected through hyperedge HE-8 (induced by source paper "evaluating noisy optimisation algorithms first hitting time is problematic"):
  with members:
    - "optimal resampling for the noisy onemax problem"

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)