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 "locomotion planning through a hybrid bayesian trajectory optimization"):
  Abstract: Locomotion planning for legged systems requires reasoning about suitable contact schedules. The contact sequence and timings constitute a hybrid dynamical system and prescribe a subset of achievable motions. State-of-the-art approaches cast motion planning as an optimal control problem. In order to decrease computational complexity, one common strategy separates footstep planning from motion optimization and plans contacts using heuristics. In this paper, we propose to learn contact schedule selection from high-level task descriptors using Bayesian optimization. A bi-level optimization is defined in which a Gaussian process model predicts the performance of trajectories generated by a motion planning nonlinear program. The agent, therefore, retains the ability to reason about suitable contact schedules, while explicit computation of the corresponding gradients is avoided. We delineate the algorithm in its general form and provide results for planning single-legged hopping. Our method is capable of learning contact schedule transitions that align with human intuition. It performs competitively against a heuristic baseline in predicting task appropriate contact schedules.

Includes members:
  Member "learning a structured neural network policy for a hopping task":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variable impedance control in end effector space an action space for reinforcement learning in contact rich tasks")
  Member "sequential linear quadratic optimal control for nonlinear switched systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adapt zero shot adaptive policy transfer for stochastic dynamical systems")
  Member "bayesian optimization using domain knowledge on the atrias biped":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning fast adaptation with meta strategy optimization")
  Member "reactive planar manipulation with convex hybrid mpc":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tactile dexterity manipulation primitives with tactile feedback")
  Member "an efficient optimal planning and control framework for quadrupedal locomotion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mpc net a first principles guided policy search")

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)