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 generalizable locomotion skills with hierarchical reinforcement learning"):
  Abstract: Learning to locomote to arbitrary goals on hardware remains a challenging problem for reinforcement learning. In this paper, we present a hierarchical learning framework that improves sample-efficiency and generalizability of locomotion skills on real-world robots. Our approach divides the problem of goal-oriented locomotion into two sub-problems: learning diverse primitives skills, and using model-based planning to sequence these skills. We parametrize our primitives as cyclic movements, improving sample-efficiency of learning on a 18 degrees of freedom robot. Then, we learn coarse dynamics models over primitive cycles and use them in a model predictive control framework. This allows us to learn to walk to arbitrary goals up to 12m away, after about two hours of training from scratch on hardware. Our results on a Daisy hexapod hardware and simulation demonstrate the efficacy of our approach at reaching distant targets, in different environments and with sensory noise.

Includes members:
  Member "data efficient reinforcement learning for legged robots":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "curious ilqr resolving uncertainty in model based rl":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "action for better prediction")
  Member "mcp learning composable hierarchical control with multiplicative compositional policies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchically decoupled imitation for morphological transfer")
  Member "bayesian optimization in variational latent spaces with dynamic compression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the differentiable cross entropy method")
  Member "learning agile and dynamic motor skills for legged robots":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning")
  Member "using deep reinforcement learning to learn high level policies on the atrias biped":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning")
  Member "learning flexible and reusable locomotion primitives for a microrobot":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning directed locomotion in modular robots with evolvable morphologies")
  Member "sim to real learning agile locomotion for quadruped robots":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to fly via deep model based reinforcement learning")

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.RO(Robotics)