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 "nonsmooth optimal value and policy functions for mechanical systems subject to unilateral constraints"):
  Abstract: State-of-the-art approaches to optimal control of contact-rich robot dynamics use smooth approximations of value and policy functions and gradient-based algorithms for improving approximator parameters. Unfortunately, the dynamics of mechanical systems subject to unilateral constraints--i.e. robot locomotion and manipulation--are generally nonsmooth. We show that value and policy functions generally inherit regularity properties like (non)smoothness from the underlying system's dynamics, and demonstrate this effect in a simple mechanical system. We conclude with a discussion of implications for the use of gradient-based algorithms for optimal control of contact-rich robot dynamics.

Includes members:
  Member "contact implicit trajectory optimization using orthogonal collocation":
    also connected through other hyperedges:
      (none)
  Member "a hybrid systems model for simple manipulation and self manipulation systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reactive navigation in partially familiar planar environments using semantic perceptual feedback")
  Member "decoupled limbs yield differentiable trajectory outcomes through intermittent contact in locomotion and manipulation":
    also connected through other hyperedges:
      (none)
  Member "trust region policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "end to end training of deep visuomotor policies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "infinite horizon policy gradient estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi step model agnostic meta learning convergence and improved 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.RO(Robotics)