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 "hierarchical reinforcement learning for quadruped locomotion"):
  Abstract: Legged locomotion is a challenging task for learning algorithms, especially when the task requires a diverse set of primitive behaviors. To solve these problems, we introduce a hierarchical framework to automatically decompose complex locomotion tasks. A high-level policy issues commands in a latent space and also selects for how long the low-level policy will execute the latent command. Concurrently, the low-level policy uses the latent command and only the robot's on-board sensors to control the robot's actuators. Our approach allows the high-level policy to run at a lower frequency than the low-level one. We test our framework on a path-following task for a dynamic quadruped robot and we show that steering behaviors automatically emerge in the latent command space as low-level skills are needed for this task. We then show efficient adaptation of the trained policy to a different task by transfer of the trained low-level policy. Finally, we validate the policies on a real quadruped robot. To the best of our knowledge, this is the first application of end-to-end hierarchical learning to a real robotic locomotion task.

Includes members:
  Member "hierarchical reinforcement learning via advantage weighted information maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multimodal trajectory optimization for motion planning")
  Member "learning agile and dynamic motor skills for legged robots":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning")
  Member "policies modulating trajectory generators":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multiplicative controller fusion a hybrid navigation strategy for deployment in unknown environments")
  Member "hierarchical reinforcement learning with hindsight":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "language as an abstraction for hierarchical deep reinforcement learning")
  Member "simple random search provides a competitive approach to reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "policy transfer with strategy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "increasing generality in machine learning through procedural content generation")
  Member "sim to real learning agile locomotion for quadruped robots":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "latent space policies for hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reinforcement learning for combinatorial optimization a 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)