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: "analysis of vanilla rolling horizon evolution parameters in general video game playing"
  Abstract: Monte Carlo Tree Search techniques have generally dominated General Video Game Playing, but recent research has started looking at Evolutionary Algorithms and their potential at matching Tree Search level of play or even outperforming these methods. Online or Rolling Horizon Evolution is one of the options available to evolve sequences of actions for planning in General Video Game Playing, but no research has been done up to date that explores the capabilities of the vanilla version of this algorithm in multiple games. This study aims to critically analyse the different configurations regarding population size and individual length in a set of 20 games from the General Video Game AI corpus. Distinctions are made between deterministic and stochastic games, and the implications of using superior time budgets are studied. Results show that there is scope for the use of these techniques, which in some configurations outperform Monte Carlo Tree Search, and also suggest that further research in these methods could boost their performance.

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 "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"
    - "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"
    - "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 "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"
    - "population seeding techniques for rolling horizon evolution 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:
    - "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"
    - "bandit based random mutation hill climbing"
    - "unifying count based exploration and intrinsic motivation"

Connected through hyperedge HE-5 (induced by source paper "population seeding techniques for rolling horizon evolution in general video game playing"):
  with members:
    - "rolling horizon coevolutionary planning for two player video games"
    - "the arcade learning environment an evaluation platform 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.AI(Artificial Intelligence)