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: "a robust walking controller based on online step location and duration optimization for bipedal locomotion"
  Abstract: Step adjustment for humanoid robots has been shown to improve gait robustness, while timing adjustment is often neglected in control strategies. In this paper, a new walking controller is proposed that combines both step location and timing adjustment for generating robust gaits. In this approach, step location and timing are decided, based on the Divergent Component of Motion (DCM) measurement. We define the DCM offset as the offset between the DCM and landing location of the swing foot at landing time, and employ it to split state space into viable/non-viable parts. Constructing our walking controller based on the DCM offset, we can exploit the whole capability of a biped robot in terms of stepping to recover from disturbances. The proposed approach is comprised of two stages. In the first stage, the nominal step location and step duration for the next step(s) are decided. In this stage, the main goal is to schedule the gait variables far from constraint boundaries for a desired walking velocity. The second stage adapts at each control cycle the landing position and time of the swing foot. By using the DCM offset and a change of variable for the step timing, we can formulate the second stage of our controller as a small sized quadratic program without the need to preview several steps ahead. To map the adapted gait variables to the full robot, a hierarchical inverse dynamics is employed. Interestingly, our approach does not require precise control of the center of pressure and can also be used on robots with passive ankles or point feet. Simulation experiments show a significant improvement in robustness to various types of external disturbances, such as pushes and slippage, compared to state of the art preview controllers where step timing is not adjusted. In particular, we demonstrate robust walking behavior for a simulated robot with passive ankles. Keywords

Connected through hyperedge HE-1 (induced by source paper "a whole body model predictive control scheme including external contact forces and com height variations"):
  with members:
    - "walking stabilization using step timing and location adjustment on the humanoid robot atlas"
    - "a mpc walking framework with external contact forces"
    - "pattern generation for walking on slippery terrains"
    - "stepping stabilization using a combination of dcm tracking and step adjustment"

Connected through hyperedge HE-2 (induced by source paper "a reactive and efficient walking pattern generator for robust bipedal locomotion"):
  with members:
    - "walking stabilization using step timing and location adjustment on the humanoid robot atlas"
    - "pattern generation for walking on slippery terrains"
    - "robust bipedal locomotion control based on model predictive control and divergent component of motion"
    - "push recovery of a humanoid robot based on model predictive control and capture point"
    - "step timing adjustment a step toward generating robust gaits"
    - "stepping stabilization using a combination of dcm tracking and step adjustment"

Connected through hyperedge HE-3 (induced by source paper "pattern generation for walking on slippery terrains"):
  with members:
    - "step timing adjustment a step toward generating robust gaits"
    - "stepping stabilization using a combination of dcm tracking and step adjustment"

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)