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: "a continuous information gain measure to find the most discriminatory problems for ai benchmarking"
  Abstract: This paper introduces an information-theoretic method for selecting a small subset of problems which gives us the most information about a group of problem-solving algorithms. This method was tested on the games in the General Video Game AI (GVGAI) framework, allowing us to identify a smaller set of games that still gives a large amount of information about the game-playing agents. This approach can be used to make agent testing more efficient in the future. We can achieve almost as good discriminatory accuracy when testing on only a handful of games as when testing on more than a hundred games, something which is often computationally infeasible. Furthermore, this method can be extended to study the dimensions of effective variance in game design between these games, allowing us to identify which games differentiate between agents in the most complementary ways. As a side effect of this investigation, we provide an up-to-date comparison on agent performance for all GVGAI games, and an analysis of correlations between scores and win-rates across both games and agents.

Connected through hyperedge HE-1 (induced by source paper "ensemble decision systems for general video game playing"):
  with members:
    - "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"
    - "population seeding techniques for rolling horizon evolution in general video game playing"

Connected through hyperedge HE-2 (induced by source paper "agent based adaptive level generation for dynamic difficulty adjustment in angry birds"):
  with members:
    - "the 2017 aibirds competition"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "a bayesian ensemble regression framework on the angry birds game"
    - "towards a theory of mind inspired generic decision making framework"

Connected through hyperedge HE-3 (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"
    - "learning to play general video games via an object embedding network"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "the n tuple bandit evolutionary algorithm for automatic game improvement"
    - "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)