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: "pattern generation for walking on slippery terrains"
  Abstract: In this paper, we extend state of the art Model Predictive Control (MPC) approaches to generate safe bipedal walking on slippery surfaces. In this setting, we formulate walking as a trade off between realizing a desired walking velocity and preserving robust foot-ground contact. Exploiting this formulation inside MPC, we show that safe walking on various flat terrains can be achieved by compromising three main attributes, i. e. walking velocity tracking, the Zero Moment Point (ZMP) modulation, and the Required Coefficient of Friction (RCoF) regulation. Simulation results show that increasing the walking velocity increases the possibility of slippage, while reducing the slippage possibility conflicts with reducing the tip-over possibility of the contact and vice versa.

Connected through hyperedge HE-1 (induced by source paper "robust humanoid locomotion using trajectory optimization and sample efficient learning"):
  with members:
    - "locomotion planning through a hybrid bayesian trajectory optimization"
    - "differential dynamic programming for multi phase rigid contact dynamics"
    - "walking control based on step timing adaptation"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "automatic lqr tuning based on gaussian process global optimization"
    - "sample efficient optimization for learning controllers for bipedal locomotion"
    - "robots that can adapt like animals"
    - "trajectory generation for multi contact momentum control"

Connected through hyperedge HE-2 (induced by source paper "trajectory optimization for robust humanoid locomotion with sample efficient learning"):
  with members:
    - "bayesian optimization using domain knowledge on the atrias biped"

Connected through hyperedge HE-3 (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"
    - "a robust walking controller based on online step location and duration optimization for bipedal locomotion"
    - "stepping stabilization using a combination of dcm tracking and step adjustment"

Connected through hyperedge HE-4 (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"
    - "a robust walking controller based on online step location and duration optimization for bipedal locomotion"
    - "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"

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)