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 "learning to plan hierarchically from curriculum"):
  Abstract: We present a framework for learning to plan hierarchically in domains with unknown dynamics. We enhance planning performance by exploiting problem structure in several ways: First, we simplify the search over plans by leveraging knowledge of skill objectives; second, shorter plans are generated by enforcing aggressively hierarchical planning; and third, we learn transition dynamics with sparse local models for better generalization. Our framework decomposes transition dynamics into skill effects and success conditions, which allows fast planning by reasoning on effects, while learning conditions from interactions with the world. We propose a simple method for learning new abstract skills, using successful trajectories stemming from completing the goals of a curriculum. Learned skills are then refined to leverage other abstract skills and enhance subsequent planning. We show that both conditions and abstract skills can be learned simultaneously while planning, even in stochastic domains. Our method is validated in experiments of increasing complexity, with up to   $2^{100}$   states, showing superior planning to classic non-hierarchical planners or reinforcement learning methods. Applicability to real-world problems is demonstrated in a simulation-to-real transfer experiment on a robotic manipulator.

Includes members:
  Member "hierarchical reinforcement learning for zero shot generalization with subtask dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta reinforcement learning with autonomous inference of subtask dependencies")
  Member "action categorization for computationally improved task learning and planning":
    also connected through other hyperedges:
      (none)
  Member "independently controllable features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the sensory commutativity of action sequences for embodied agents")
  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 "independently controllable factors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")
  Member "despot online pomdp planning with regularization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient uncertainty aware decision making for automated driving using guided branching")
  Member "playing atari with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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.AI(Artificial Intelligence)