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 "robust humanoid contact planning with learned zero and one step capturability prediction"):
  Abstract: Humanoid robots maintain balance and navigate by controlling the contact wrenches applied to the environment. While it is possible to plan dynamically-feasible motion that applies appropriate wrenches using existing methods, a humanoid may also be affected by external disturbances. Existing systems typically rely on controllers to reactively recover from disturbances. However, such controllers may fail when the robot cannot reach contacts capable of rejecting a given disturbance. In this paper, we propose a search-based footstep planner which aims to maximize the probability of the robot successfully reaching the goal without falling as a result of a disturbance. The planner considers not only the poses of the planned contact sequence, but also alternative contacts near the planned contact sequence that can be used to recover from external disturbances. Although this additional consideration significantly increases the computation load, we train neural networks to efficiently predict multi-contact zero-step and one-step capturability, which allows the planner to generate robust contact sequences efficiently. Our results show that our approach generates footstep sequences that are more robust to external disturbances than a conventional footstep planner in four challenging scenarios.

Includes members:
  Member "unified multi contact fall mitigation planning for humanoids via contact transition tree optimization":
    also connected through other hyperedges:
      (none)
  Member "efficient humanoid contact planning using learned centroidal dynamics prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "accurate vision based manipulation through contact reasoning")
  Member "capturability based pattern generation for walking with variable height":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "biped stabilization by linear feedback of the variable height inverted pendulum model")
  Member "walking stabilization using step timing and location adjustment on the humanoid robot atlas":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online dcm trajectory generation for push recovery of torque controlled humanoid robots")
  Member "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization":
    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")
  Member "a mpc walking framework with external contact forces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning generalizable locomotion skills with hierarchical reinforcement learning")
  Member "structured contact force optimization for kino dynamic motion generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "accelerated admm based trajectory optimization for legged locomotion with coupled rigid body dynamics")
  Member "step timing adjustment a step toward generating robust gaits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "online dcm trajectory generation for push recovery of torque controlled humanoid robots")

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)