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 "clic curriculum learning and imitation for object control in non rewarding environments"):
  Abstract: In this paper we study a new reinforcement learning setting where the environment is non-rewarding, contains several possibly related objects of various controllability, and where an apt agent Bob acts independently, with non-observable intentions. We argue that this setting defines a realistic scenario and we present a generic discrete-state discrete-action model of such environments. To learn in this environment, we propose an unsupervised reinforcement learning agent called CLIC for Curriculum Learning and Imitation for Control. CLIC learns to control individual objects in its environment, and imitates Bob's interactions with these objects. It selects objects to focus on when training and imitating by maximizing its learning progress. We show that CLIC is an effective baseline in our new setting. It can effectively observe Bob to gain control of objects faster, even if Bob is not explicitly teaching. It can also follow Bob when he acts as a mentor and provides ordered demonstrations. Finally, when Bob controls objects that the agent cannot, or in presence of a hierarchy between objects in the environment, we show that CLIC ignores non-reproducible and already mastered interactions with objects, resulting in a greater benefit from imitation.

Includes members:
  Member "learning actionable representations with goal conditioned policies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")
  Member "curriculum learning by transfer learning theory and experiments with deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey")
  Member "large scale study of curiosity driven learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "learning curriculum policies for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey")
  Member "diversity is all you need learning skills without a reward function":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning curiosity algorithms")
  Member "unsupervised control through non parametric discriminative rewards":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "many goals reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaling all goals updates in reinforcement learning using convolutional neural networks")
  Member "curious intrinsically motivated modular multi goal reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "automatic curriculum learning for deep rl a short survey")

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)