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: "robust optimal planning and control of non periodic bipedal locomotion with a centroidal momentum model"
  Abstract: This study presents a theoretical method for planning and controlling agile bipedal locomotion based on robustly tracking a set of non-periodic keyframe states. Based on centroidal momentum dynamics, we formulate a hybrid phase-space planning and control method which includes the following key components: (i) a step transition solver that enables dynamically tracking non-periodic keyframe states over various types of terrains, (ii) a robust hybrid automaton to effectively formulate planning and control algorithms, (iii) a steering direction model to control the robot's heading, (iv) a phase-space metric to measure distance to the planned locomotion manifolds, and (v) a hybrid control method based on the previous distance metric to produce robust dynamic locomotion under external disturbances. Compared to other locomotion methodologies, we have a large focus on non-periodic gait generation and robustness metrics to deal with disturbances. Such focus enables the proposed control method to robustly track non-periodic keyframe states over various challenging terrains and under external disturbances as illustrated through several simulations.

Connected through hyperedge HE-1 (induced by source paper "nonlinear model predictive control for robust bipedal locomotion exploring com height and angular momentum changes"):
  with members:
    - "a whole body model predictive control scheme including external contact forces and com height variations"
    - "on time optimisation of centroidal momentum dynamics"
    - "robust bipedal locomotion control based on model predictive control and divergent component of motion"
    - "dynamic walking over rough terrains by nonlinear predictive control of the floating base inverted pendulum"

Connected through hyperedge HE-2 (induced by source paper "beyond basins of attraction evaluating robustness of natural dynamics"):
  with members:
    - "walking with confidence safety regulation for full order biped models"
    - "gait learning for soft microrobots controlled by light fields"
    - "learning from outside the viability kernel why we should build robots that can fall with grace"
    - "real time control for autonomous racing based on viability theory"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "shaping in practice training wheels to learn fast hopping directly in hardware"
    - "hamilton jacobi reachability a brief overview and recent advances"
    - "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"

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)