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 "hyper meta reinforcement learning with sparse reward"):
  Abstract: Despite their success, existing meta reinforcement learning methods still have difficulty in learning a meta policy effectively for RL problems with sparse reward. To this end, we develop a novel meta reinforcement learning framework, Hyper-Meta RL (HMRL), for sparse reward RL problems. It consists of meta state embedding, meta reward shaping and meta policy learning modules: The cross-environment meta state embedding module constructs a common meta state space to adapt to different environments; The meta state based environment-specific meta reward shaping effectively extends the original sparse reward trajectory by cross-environmental knowledge complementarity; As a consequence, the meta policy then achieves better generalization and efficiency with the shaped meta reward. Experiments with sparse reward show the superiority of HMRL on both transferability and policy learning efficiency.

Includes members:
  Member "meta reinforcement learning with task embedding and shared policy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "solving rubik s cube with a robot hand")
  Member "meta inverse reinforcement learning with probabilistic context variables":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "squirl robust and efficient learning from video demonstration of long horizon robotic manipulation tasks")
  Member "dealing with sparse rewards in reinforcement learning":
    also connected through other hyperedges:
      (none)
  Member "mame model agnostic meta exploration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "curriculum in gradient based meta reinforcement learning")
  Member "norml no reward meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning")
  Member "reward shaping via meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "balance between efficient and effective learning dense2sparse reward shaping for robot manipulation with environment uncertainty")
  Member "meta gradient reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meta learning curiosity algorithms")
  Member "meta reinforcement learning of structured exploration strategies":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)