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 "riemannian motion policy fusion through learnable lyapunov function reshaping"):
  Abstract: RMPflow is a recently proposed policy-fusion framework based on differential geometry. While RMPflow has demonstrated promising performance, it requires the user to provide sensible subtask policies as Riemannian motion policies (RMPs: a motion policy and an importance matrix function), which can be a difficult design problem in its own right. We propose RMPfusion, a variation of RMPflow, to address this issue. RMPfusion supplements RMPflow with weight functions that can hierarchically reshape the Lyapunov functions of the subtask RMPs according to the current configuration of the robot and environment. This extra flexibility can remedy imperfect subtask RMPs provided by the user, improving the combined policy's performance. These weight functions can be learned by back-propagation. Moreover, we prove that, under mild restrictions on the weight functions, RMPfusion always yields a globally Lyapunov-stable motion policy. This implies that we can treat RMPfusion as a structured policy class in policy optimization that is guaranteed to generate stable policies, even during the immature phase of learning. We demonstrate these properties of RMPfusion in imitation learning experiments both in simulation and on a real-world robot.

Includes members:
  Member "stable concurrent controller composition for multi objective robotic tasks":
    also connected through other hyperedges:
      (none)
  Member "representing robot task plans as robust logical dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transferable task execution from pixels through deep planning domain learning")
  Member "multi objective policy generation for multi robot systems using riemannian motion policies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stable concurrent controller composition for multi objective robotic tasks")
  Member "learning latent space dynamics for tactile servoing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning when to trust a dynamics model for planning in reduced state spaces")
  Member "riemannian motion policies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "camera to robot pose estimation from a single image")
  Member "rmpflow a computational graph for automatic motion policy generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "camera to robot pose estimation from a single image")
  Member "convergence of value aggregation for imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on policy robot imitation learning from a converging supervisor")
  Member "fast policy learning through imitation and reinforcement":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "non monotonic sequential text generation")

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)