human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "heuristic planning for rough terrain locomotion in presence of external disturbances and variable perception quality"):
  with members:
    - "application of wrench based feasibility analysis to the online trajectory optimization of legged robots"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"
    - "planning and execution of dynamic whole body locomotion for a hydraulic quadruped on challenging terrain"
    - "on line and on board planning and perception for quadrupedal locomotion"

Connected through hyperedge HE-2 (induced by source paper "crocoddyl an efficient and versatile framework for multi contact optimal control"):
  with members:
    - "differential dynamic programming for multi phase rigid contact dynamics"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"
    - "a family of iterative gauss newton shooting methods for nonlinear optimal control"
    - "an efficient optimal planning and control framework for quadrupedal locomotion"
    - "trajectory optimization through contacts and automatic gait discovery for quadrupeds"
    - "balancing experiments on a torque controlled humanoid with hierarchical inverse dynamics"

Connected through hyperedge HE-3 (induced by source paper "passive whole body control for quadruped robots experimental validation over challenging terrain"):
  with members:
    - "heuristic planning for rough terrain locomotion in presence of external disturbances and variable perception quality"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"
    - "real time motion planning of legged robots a model predictive control approach"
    - "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"
    - "an efficiently solvable quadratic program for stabilizing dynamic locomotion"

Connected through hyperedge HE-4 (induced by source paper "differential dynamic programming for multi phase rigid contact dynamics"):
  with members:
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"
    - "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"
    - "planning and execution of dynamic whole body locomotion for a hydraulic quadruped on challenging terrain"
    - "trajectory generation for multi contact momentum control"

Connected through hyperedge HE-5 (induced by source paper "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"):
  with members:
    - "the actuation consistent wrench polytope awp and the feasible wrench polytope fwp"
    - "fast trajectory optimization for legged robots using vertex based zmp constraints"
    - "trajectory optimization through contacts and automatic gait discovery for quadrupeds"
    - "hierarchical planning of dynamic movements without scheduled contact sequences"
    - "planning and execution of dynamic whole body locomotion for a hydraulic quadruped on challenging terrain"

Question: Please tell me which class the center node 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)