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: "instrument orientation based metrics for surgical skill evaluation in robot assisted and open needle driving"
  Abstract: The technical skill of surgeons directly impacts patient outcomes. Advanced tracking systems enable the development of objective motion-based metrics for skill evaluation, but these metrics are not sufficient to evaluate the performance in complex surgical tasks. In this study, we developed metrics for surgical skill evaluation that are based on the orientation of the surgical instruments. Experienced robotic surgeons and novice users performed teleoperated (using the da Vinci Research Kit) and open needle-driving. Task time and the rate of orientation change successfully distinguished between experienced surgeons and novice users. Path length and the normalized angular displacement allowed for a good separation only in part of the experiment. Our new promising metrics for surgical skill evaluation captured technical aspects that are taught during surgeons' training. They provide complementing evaluation to those of classical metrics. Orientation-based metrics add value to skill assessment and may be an adjunct to classic objective metrics providing more granular discrimination of skills.

Connected through hyperedge HE-1 (induced by source paper "deep learning with convolutional neural network for objective skill evaluation in robot assisted surgery"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "deep learning for time series analysis"
    - "automated surgical skill assessment in rmis training"
    - "data augmentation of wearable sensor data for parkinson s disease monitoring using convolutional neural networks"
    - "recognizing surgical activities with recurrent neural networks"
    - "multi scale convolutional neural networks for time series classification"
    - "deep learning in neural networks"
    - "max pooling dropout for regularization of convolutional neural networks"

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)