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: "population seeding techniques for rolling horizon evolution in general video game playing"
  Abstract: While Monte Carlo Tree Search and closely related methods have dominated General Video Game Playing, recent research has demonstrated the promise of Rolling Horizon Evolutionary Algorithms as an interesting alternative. However, there is little attention paid to population initialization techniques in the setting of general real-time video games. Therefore, this paper proposes the use of population seeding to improve the performance of Rolling Horizon Evolution and presents the results of two methods, One Step Look Ahead and Monte Carlo Tree Search, tested on 20 games of the General Video Game AI corpus with multiple evolution parameter values (population size and individual length). An in-depth analysis is carried out between the results of the seeding methods and the vanilla Rolling Horizon Evolution. In addition, the paper presents a comparison to a Monte Carlo Tree Search algorithm. The results are promising, with seeding able to boost performance significantly over baseline evolution and even match the high level of play obtained by the Monte Carlo Tree Search.

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 "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"
    - "the n tuple bandit evolutionary algorithm for game agent optimisation"
    - "generative design in minecraft gdmc settlement generation competition"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "ensemble decision systems for general video game playing"):
  with members:
    - "a continuous information gain measure to find the most discriminatory problems for ai benchmarking"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "deceptive games"
    - "analysis of vanilla rolling horizon evolution parameters in general video game playing"

Connected through hyperedge HE-4 (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"
    - "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"
    - "bandit based random mutation hill climbing"
    - "unifying count based exploration and intrinsic motivation"

Connected through hyperedge HE-5 (induced by source paper "intelligent middle level game control"):
  with members:
    - "proximal policy optimization algorithms"
    - "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)