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: "learning to design games strategic environments in deep reinforcement learning"
  Abstract: In typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy for the agent taking actions through its interactions with the environment. In this paper, we extend this setting by considering the environment is not given, but controllable and learnable through its interaction with the agent at the same time. Theoretically, we find a dual Markov decision process (MDP) w.r.t. the environment to that w.r.t. the agent, and solving the dual MDP-policy pair yields a policy gradient solution to optimizing the parametrized environment. Furthermore, environments with discontinuous parameters are addressed by a proposed general generative framework. While the idea is illustrated by an extended two-agent rock-paper-scissors game, our experiments on a Maze game design task show the effectiveness of the proposed algorithm in generating diverse and challenging Mazes against different agents with various settings.

Connected through hyperedge HE-1 (induced by source paper "layout design for intelligent warehouse by evolution with fitness approximation"):
  with members:
    - "tvm end to end optimization stack for deep learning"
    - "regularized evolution for image classifier architecture search"
    - "accelerating neural architecture search using performance prediction"
    - "progressive neural architecture search"
    - "efficient architecture search by network transformation"
    - "searching for activation functions"
    - "neural optimizer search with reinforcement learning"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "dynamic fusion networks for machine reading comprehension"):
  with members:
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "making neural qa as simple as possible but not simpler"
    - "fusionnet fusing via fully aware attention with application to machine comprehension"
    - "reasonet learning to stop reading in machine comprehension"
    - "bilateral multi perspective matching for natural language sentences"
    - "reading wikipedia to answer open domain questions"
    - "newsqa a machine comprehension dataset"

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)