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 "motion planning for quadrupedal locomotion coupled planning terrain mapping and whole body control"):
  Abstract: Legged robots promise an advantage over traditional wheeled systems as they can offer greater mobility on challenging terrain. However, most legged robots are still confined to structured and flat environments. One of the main reasons for this is the difficulty in planning complex whole-body motions while taking into account the terrain conditions. Previous research in locomotion mainly focused either on generating reactive behaviors, that tackle only small terrain changes, or on kinematically planning foothold locations with partial terrain information. Alternatively, legged motion planning approaches focus on synthesizing complex whole-body motions, but often do not consider terrain characteristics. This problem is very high-dimensional as it considers the robot's dynamics together with the terrain model in a suitable problem formulation. In this work, we address these challenges by studying two different motion planning methods (decoupled and coupled foothold and motion planning), and by analyzing the effect of considering friction cones, kinematic limits and torque limits at the whole-body control level. For that, we first briefly introduce our decoupled motion planning approach. Second, we propose a novel trajectory and foothold optimization method that plans dynamically both foothold locations and CoM motions (coupled planning). This second method jointly optimizes body motion, step duration and foothold selection, considering the terrain topology. Finally, we introduce a whole-body controller that tracks compliantly trunk motions while avoiding slippage as well as kinematic and torque limits. Additionally, we impose friction cone constraints in real-time using terrain normals estimated from the terrain map. With this novel locomotion framework we can cross a wide range of terrain conditions—significantly more compared our previous approach—while we validate our novel framework on the hydraulic quadruped robot; HyQ. Additionally, our coupled motion planner can be easily generalize to various terrain conditions, thanks to a parametrized dynamic model, and an online terrain mapping that is used in our real-time whole-body controller. We report thorough experimental results and comparative evaluations over a set of terrains of progressively increasing difficulty.

Includes members:
  Member "differential dynamic programming for multi phase rigid contact dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning how to walk warm starting optimal control solver with memory of motion")
  Member "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "whole body walking generation using contact parametrization a non linear trajectory optimization approach")
  Member "structured contact force optimization for kino dynamic motion generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "accelerated admm based trajectory optimization for legged locomotion with coupled rigid body dynamics")
  Member "a convex model of momentum dynamics for multi contact motion generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "contact planning for the anymal quadruped robot using an acyclic reachability based planner")
  Member "planning and execution of dynamic whole body locomotion for a hydraulic quadruped on challenging terrain":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning how to walk warm starting optimal control solver with memory of motion")
  Member "on line and on board planning and perception for quadrupedal locomotion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "heuristic planning for rough terrain locomotion in presence of external disturbances and variable perception quality")

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)