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: "stair climbing stabilization of the hrp 4 humanoid robot using whole body admittance control"
  Abstract: This paper considers dynamic stair climbing with the HRP-4 humanoid robot as part of an Airbus manufacturing use-case demonstrator. Apart from the fact that HRP-4 had never been challenged to this scenario before, we share experimental knowledge gathered in achieving this task. We also show how walking stabilization based on linear inverted pendulum tracking can be extended with quadratic programming-based wrench distribution and a whole-body admittance controller that relies on both end-effector and CoM strategies. While existing stabilizers tend to use either one or the other of these two approaches, experiments suggest that their combination can improve tracking performance. We demonstrate this solution in a hardware experiment where HRP-4 climbs a staircase with 18.5-cm steps, and release our walking controller as open source software.

Connected through hyperedge HE-1 (induced by source paper "data efficient and safe learning for locomotion via simplified model"):
  with members:
    - "dynamic locomotion for passive ankle biped robots and humanoids using whole body locomotion control"
    - "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"
    - "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 "biped stabilization by linear feedback of the variable height inverted pendulum model"):
  with members:
    - "capturability based pattern generation for walking with variable height"
    - "dynamic walking over rough terrains by nonlinear predictive control of the floating base inverted pendulum"
    - "walking on partial footholds including line contacts with the humanoid robot atlas"
    - "multi contact walking pattern generation based on model preview control of 3d com accelerations"
    - "zmp support areas for multi contact mobility under frictional constraints"

Connected through hyperedge HE-3 (induced by source paper "sensitivity of legged balance control to uncertainties and sampling period"):
  with members:
    - "feedback mpc for torque controlled legged robots"

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)