human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "intrinsic motivation and automatic curricula via asymmetric self play"):
  Abstract: We describe a simple scheme that allows an agent to learn about its environment in an unsupervised manner. Our scheme pits two versions of the same agent, Alice and Bob, against one another. Alice proposes a task for Bob to complete; and then Bob attempts to complete the task. In this work we will focus on two kinds of environments: (nearly) reversible environments and environments that can be reset. Alice will "propose" the task by doing a sequence of actions and then Bob must undo or repeat them, respectively. Via an appropriate reward structure, Alice and Bob automatically generate a curriculum of exploration, enabling unsupervised training of the agent. When Bob is deployed on an RL task within the environment, this unsupervised training reduces the number of supervised episodes needed to learn, and in some cases converges to a higher reward.

Includes members:
  Member "adversarial variational bayes unifying variational autoencoders and generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial code learning for image generation")
  Member "automatic goal generation for reinforcement learning agents":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning curiosity algorithms")
  Member "reverse curriculum generation for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey")
  Member "adversarial learning for neural dialogue generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "non autoregressive neural dialogue generation")
  Member "curiosity driven exploration by self supervised prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "torchcraft a library for machine learning research on real time strategy games":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "why build an assistant in minecraft")
  Member "unifying count based exploration and intrinsic motivation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "benchmarking deep reinforcement learning for continuous control":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")

Question: Please tell me which class the source paper 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.LG(Machine Learning)