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: "feedback control of an exoskeleton for paraplegics toward robustly stable hands free dynamic walking"
  Abstract: This paper presents a design methodology for feedback control policy that enables lower-limb exoskeletons with paraplegic patients to walk without the use of crutches. The objective is to translate formal control design methodologies from bipedal robots to exoskeleton systems to achieve dynamic hands-free walking. Built upon the unique combination of fast offline trajectory optimization and machine learning algorithms, we develop a smooth policy that provides a systematic means for coordinating limb motion and providing corrective actions to follow commands and attenuate disturbances. This approach is validated both numerically and in pre-clinical experimental testing, the latter enabling people with paraplegia to walk hands-free, with all control actions required for stable dynamic walking provided by an onboard controller.

Connected through hyperedge HE-1 (induced by source paper "human preference based learning for high dimensional optimization of exoskeleton walking gaits"):
  with members:
    - "towards variable assistance for lower body exoskeletons"
    - "preference based learning for exoskeleton gait optimization"
    - "online trajectory planning through combined trajectory optimization and function approximation application to the exoskeleton atalante"
    - "multi dueling bandits with dependent arms"
    - "online structured prediction via coactive learning"

Connected through hyperedge HE-2 (induced by source paper "towards variable assistance for lower body exoskeletons"):
  with members:
    - "control barrier functions theory and applications"

Connected through hyperedge HE-3 (induced by source paper "preference based learning for exoskeleton gait optimization"):
  with members:
    - "dueling posterior sampling for preference based reinforcement learning"
    - "multi dueling bandits with dependent arms"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "concrete problems in ai safety"
    - "online structured prediction via coactive learning"

Connected through hyperedge HE-4 (induced by source paper "compliance shaping for control of strength amplification exoskeletons with elastic cuffs"):
  with members:
    - "complex stiffness model of physical human robot interaction implications for control of performance augmentation exoskeletons"
    - "modeling and loop shaping of single joint amplification exoskeleton with contact sensing and series elastic actuation"

Connected through hyperedge HE-5 (induced by source paper "rapid trajectory optimization using c frost with illustration on a cassie series dynamic walking biped"):
  with members:
    - "feedback control of a cassie bipedal robot walking standing and riding a segway"
    - "enhancing the performance of a safe controller via supervised learning for truck lateral control"
    - "trajectory generation for multi contact momentum control"

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)