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: "dynamic locomotion for passive ankle biped robots and humanoids using whole body locomotion control"
  Abstract: Whole-body control (WBC) is a generic task-oriented control method for feedback control of loco-manipulation behaviors in humanoid robots. The combination of WBC and model-based walking controllers has been widely utilized in various humanoid robots. However, to date, the WBC method has not been employed for unsupported passive-ankle dynamic locomotion. As such, in this paper, we devise a new WBC, dubbed whole-body locomotion controller (WBLC), that can achieve experimental dynamic walking on unsupported passive-ankle biped robots. A key aspect of WBLC is the relaxation of contact constraints such that the control commands produce reduced jerk when switching foot contacts. To achieve robust dynamic locomotion, we conduct an in-depth analysis of uncertainty for our dynamic walking algorithm called time-to-velocity-reversal (TVR) planner. The uncertainty study is fundamental as it allows us to improve the control algorithms and mechanical structure of our robot to fulfill the tolerated uncertainty. In addition, we conduct extensive experimentation for: 1) unsupported dynamic balancing (i.e. in-place stepping) with a six degree-of-freedom (DoF) biped, Mercury; 2) unsupported directional walking with Mercury; 3) walking over an irregular and slippery terrain with Mercury; and 4) in-place walking with our newly designed ten-DoF viscoelastic liquid-cooled biped, DRACO. Overall, the main contributions of this work are on: a) achieving various modalities of unsupported dynamic locomotion of passive-ankle bipeds using a WBLC controller and a TVR planner, b) conducting an uncertainty analysis to improve the mechanical structure and the controllers of Mercury, and c) devising a whole-body control strategy that reduces movement jerk during walking.

Connected through hyperedge HE-1 (induced by source paper "data efficient and safe learning for locomotion via simplified model"):
  with members:
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "learning symmetric and low energy locomotion"
    - "model ensemble trust region policy optimization"
    - "stair climbing stabilization of the hrp 4 humanoid robot using whole body admittance control"
    - "fast kinodynamic bipedal locomotion planning with moving obstacles"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-2 (induced by source paper "stance locomotion adaptation over soft terrain"):
  with members:
    - "heuristic planning for rough terrain locomotion in presence of external disturbances and variable perception quality"
    - "frequency aware model predictive control"
    - "passive whole body control for quadruped robots experimental validation over challenging terrain"
    - "contact implicit optimization of locomotion trajectories for a quadrupedal microrobot"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "robust whole body motion control of legged robots"
    - "robot impedance control and passivity analysis with inner torque and velocity feedback loops"
    - "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"

Connected through hyperedge HE-3 (induced by source paper "highly dynamic quadruped locomotion via whole body impulse control and model predictive control"):
  with members:
    - "frequency aware model predictive control"
    - "learning agile and dynamic motor skills for legged robots"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "stability of surface contacts for humanoid robots closed form formulae of the contact wrench cone for rectangular support areas"

Connected through hyperedge HE-4 (induced by source paper "control of a high performance bipedal robot using viscoelastic liquid cooled actuators"):
  with members:
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "fast kinodynamic bipedal locomotion planning with moving obstacles"
    - "yet another tutorial of disturbance observer robust stabilization and recovery of nominal performance"

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)