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 game agent optimisation"
  Abstract: This paper describes the N-Tuple Bandit Evolutionary Algorithm (NTBEA), an optimisation algorithm developed for noisy and expensive discrete (combinatorial) optimisation problems. The algorithm is applied to two game-based hyper-parameter optimisation problems. The N-Tuple system directly models the statistics, approximating the fitness and number of evaluations of each modelled combination of parameters. The model is simple, efficient and informative. Results show that the NTBEA significantly outperforms grid search and an estimation of distribution 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"
    - "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:
    - "efficient evolutionary methods for game agent optimisation model based is best"
    - "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:
    - "efficient evolutionary methods for game agent optimisation model based is best"
    - "the multi agent reinforcement learning in malmo marlo competition"
    - "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 "efficient evolutionary methods for game agent optimisation model based is best"):
  with members:
    - "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"
    - "bandit based random mutation hill climbing"

Connected through hyperedge HE-5 (induced by source paper "learning local forward models on unforgiving games"):
  with members:
    - "efficient evolutionary methods for game agent optimisation model based is best"
    - "a local approach to forward model learning results on the game of life game"
    - "imagination augmented agents for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "game ai research with fast planet wars variants"):
  with members:
    - "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"
    - "the n tuple bandit evolutionary algorithm for automatic game improvement"
    - "revisiting the arcade learning environment evaluation protocols and open problems 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.NE(Neural and Evolutionary Computing)