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: "bandit based random mutation hill climbing"
  Abstract: The Random Mutation Hill-Climbing algorithm is a direct search technique mostly used in discrete domains. It repeats the process of randomly selecting a neighbour of a best-so-far solution and accepts the neighbour if it is better than or equal to it. In this work, we propose to use a novel method to select the neighbour solution using a set of independent multi- armed bandit-style selection units which results in a bandit-based Random Mutation Hill-Climbing algorithm. The new algorithm significantly outperforms Random Mutation Hill-Climbing in both OneMax (in noise-free and noisy cases) and Royal Road problems (in the noise-free case). The algorithm shows particular promise for discrete optimisation problems where each fitness evaluation is expensive.

Connected through hyperedge HE-1 (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"
    - "the n tuple bandit evolutionary algorithm for automatic game improvement"

Connected through hyperedge HE-2 (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"
    - "the n tuple bandit evolutionary algorithm for automatic game improvement"
    - "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"
    - "unifying count based exploration and intrinsic motivation"

Connected through hyperedge HE-3 (induced by source paper "evolving game skill depth using general video game ai agents"):
  with members:
    - "the n tuple bandit evolutionary algorithm for automatic game improvement"
    - "optimal resampling for the noisy onemax problem"
    - "rolling horizon coevolutionary planning for two player video games"

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)