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 "planning with abstract learned models while learning transferable subtasks"):
  Abstract: We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction. We call this framework Planning with Abstract Learned Models (PALM). By representing subtasks symbolically using a new formal structure, the lifted abstract Markov decision process (L-AMDP), PALM learns models that are independent and modular. Through our experiments, we show how PALM integrates planning and execution, facilitating a rapid and efficient learning of abstract, hierarchical models. We also demonstrate the increased potential for learned models to be transferred to new and related tasks.

Includes members:
  Member "an investigation of model free planning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "offworld gym open access physical robotics environment for real world reinforcement learning benchmark and research")
  Member "learning representations in model free hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement learning for combinatorial optimization a survey")
  Member "learning multi level hierarchies with hindsight":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning functionally decomposed hierarchies for continuous control tasks")
  Member "meta learning shared hierarchies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting meta learning as supervised learning")
  Member "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "compositional planning using optimal option models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "combating the compounding error problem with a multi step model")
  Member "hierarchical reinforcement learning with the maxq value function decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning functionally decomposed hierarchies for continuous control tasks")

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)