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 lobster inspired robotic glove for hand rehabilitation"
  Abstract: This paper presents preliminary results of the design, development, and evaluation of a hand rehabilitation glove fabricated using lobster-inspired hybrid design with rigid and soft components for actuation. Inspired by the bending abdomen of lobsters, hybrid actuators are built with serially jointed rigid shells actuated by pressurized soft chambers inside to generate bending motions. Such bio-inspiration absorbs features from the classical rigid-bodied robotics with preciselydefined motion generation, as well as the emerging soft robotics with light-weight, physically safe, and adaptive actuation. The fabrication procedure is described, followed by experiments to mechanically characterize these actuators. Finally, an open-palm glove design integrated with these hybrid actuators are presented for a qualitative case study. A hand rehabilitation system is developed by learning patterns of the sEMG signals from the users forearm to train the assistive glove for hand rehabilitation exercises.

Connected through hyperedge HE-1 (induced by source paper "reconfigurable design for omni adaptive grasp learning"):
  with members:
    - "rigid soft interactive learning for robust grasping"
    - "learning dexterous in hand manipulation"
    - "a learning framework for robust bin picking by customized grippers"
    - "a reconfigurable hybrid actuator with rigid and soft components"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"

Connected through hyperedge HE-2 (induced by source paper "a lobster inspired hybrid actuator with rigid and soft components"):
  with members:
    - "a reconfigurable hybrid actuator with rigid and soft components"

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)