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 "rapid trajectory optimization using c frost with illustration on a cassie series dynamic walking biped"):
  Abstract: One of the big attractions of low-dimensional models for gait design has been the ability to compute solutions rapidly, whereas one of their drawbacks has been the difficulty in mapping the solutions back to the target robot. This paper presents a set of tools for rapidly determining solutions for ``humanoids'' without removing or lumping degrees of freedom. The main tools are (1) C-FROST, an open-source C++ interface for FROST, a direct collocation optimization tool; and (2) multi-threading. The results will be illustrated on a 20-DoF floating-base model for a Cassie-series bipedal robot through numerical calculations and physical experiments.

Includes members:
  Member "feedback control of an exoskeleton for paraplegics toward robustly stable hands free dynamic walking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "human preference based learning for high dimensional optimization of exoskeleton walking gaits")
  Member "feedback control of a cassie bipedal robot walking standing and riding a segway":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "iterative reinforcement learning based design of dynamic locomotion skills for cassie")
  Member "enhancing the performance of a safe controller via supervised learning for truck lateral control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "safe policy synthesis in multi agent pomdps via discrete time barrier functions")
  Member "trajectory generation for multi contact momentum control":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "whole body walking generation using contact parametrization a non linear trajectory optimization approach")

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)