human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "efficiently calibrating cable driven surgical robots with rgbd sensing temporal windowing and linear and recurrent neural network compensation"):
  Abstract: Automation of surgical subtasks using cable-driven robotic surgical assistants (RSAs) such as Intuitive Surgical's da Vinci Research Kit (dVRK) is challenging due to imprecision in control from cable-related effects such as backlash, stretch, and hysteresis. We propose a novel approach to efficiently calibrate a dVRK by placing a 3D printed fiducial coordinate frame on the arm and end-effector that is tracked using RGBD sensing. To measure the coupling effects between joints and history-dependent effects, we analyze data from sampled trajectories and consider 13 modeling approaches using LSTM recurrent neural networks and linear models with varying temporal window length to provide corrective feedback. With the proposed method, data collection takes 31 minutes to produce 1800 samples and model training takes less than a minute. Results suggest that the resulting model can reduce the mean tracking error of the physical robot from 2.96mm to 0.65mm on a test set of reference trajectories. We evaluate the model by executing open-loop trajectories of the FLS peg transfer surgeon training task. Results suggest that the best approach increases success rate from 39.4% to 96.7% comparable to the performance of an expert surgical resident. Supplementary material, including 3D-printable models, is available at this https URL.

Includes members:
  Member "applying depth sensing to automated surgical manipulation with a da vinci robot":
    also connected through other hyperedges:
      (none)
  Member "autonomous tissue manipulation via surgical robot using learning based model predictive control":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "applying depth sensing to automated surgical manipulation with a da vinci robot")
  Member "deep imitation learning of sequential fabric smoothing policies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning predictive representations for deformable objects using contrastive estimation")
  Member "real time data driven precision estimator for raven ii surgical robot end effector position":
    also connected through other hyperedges:
      (none)
  Member "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep imitation learning of sequential fabric smoothing from an algorithmic supervisor")
  Member "robot autonomy for surgery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "super deep a surgical perception framework for robotic tissue manipulation using deep learning for feature extraction")
  Member "model agnostic meta learning for fast adaptation of deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

Question: Please tell me which class the source paper 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)