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 "mamic macro and micro curriculum for robotic reinforcement learning"):
  Abstract: Shaping in humans and animals has been shown to be a powerful tool for learning complex tasks as compared to learning in a randomized fashion. This makes the problem less complex and enables one to solve the easier sub task at hand first. Generating a curriculum for such guided learning involves subjecting the agent to easier goals first, and then gradually increasing their difficulty. This paper takes a similar direction and proposes a dual curriculum scheme for solving robotic manipulation tasks with sparse rewards, called MaMiC. It includes a macro curriculum scheme which divides the task into multiple sub-tasks followed by a micro curriculum scheme which enables the agent to learn between such discovered sub-tasks. We show how combining macro and micro curriculum strategies help in overcoming major exploratory constraints considered in robot manipulation tasks without having to engineer any complex rewards. We also illustrate the meaning of the individual curricula and how they can be used independently based on the task. The performance of such a dual curriculum scheme is analyzed on the Fetch environments.

Includes members:
  Member "multi goal reinforcement learning challenging robotics environments and request for research":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalized hindsight for reinforcement learning")
  Member "hindsight experience replay":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "automatic goal generation for reinforcement learning agents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta learning curiosity algorithms")
  Member "imitation from observation learning to imitate behaviors from raw video via context translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "visual task progress estimation with appearance invariant embeddings for robot control and planning")
  Member "reverse curriculum generation for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey")
  Member "intrinsic motivation and automatic curricula via asymmetric self play":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "overcoming exploration in reinforcement learning with demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "stochastic neural networks for hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "meta learning curiosity algorithms")

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)