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 "super deep a surgical perception framework for robotic tissue manipulation using deep learning for feature extraction"):
  Abstract: Robotic automation in surgery requires precise tracking of surgical tools and mapping of deformable tissue. Previous works on surgical perception frameworks require significant effort in developing features for surgical tool and tissue tracking. In this work, we overcome the challenge by exploiting deep learning methods for surgical perception. We integrated deep neural networks, capable of efficient feature extraction, into the tissue reconstruction and instrument pose estimation processes. By leveraging transfer learning, the deep learning based approach requires minimal training data and reduced feature engineering efforts to fully perceive a surgical scene. The framework was tested on three publicly available datasets, which use the da Vinci Surgical System, for comprehensive analysis. Experimental results show that our framework achieves state-of-the-art tracking performance in a surgical environment by utilizing deep learning for feature extraction.

Includes members:
  Member "surfelwarp efficient non volumetric single view dynamic reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "super a surgical perception framework for endoscopic tissue manipulation with surgical robotics")
  Member "open sourced reinforcement learning environments for surgical robotics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "applying depth sensing to automated surgical manipulation with a da vinci robot")
  Member "super a surgical perception framework for endoscopic tissue manipulation with surgical robotics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "applying depth sensing to automated surgical manipulation with a da vinci robot")
  Member "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "extremely dense point correspondences using a learned feature descriptor")
  Member "augmented reality predictive displays to help mitigate the effects of delayed telesurgery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "super a surgical perception framework for endoscopic tissue manipulation with surgical robotics")
  Member "pyramid stereo matching network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "simultaneous recognition and pose estimation of instruments in minimally invasive surgery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards generalizable surgical activity recognition using spatial temporal graph convolutional networks")
  Member "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep imitation learning of sequential fabric smoothing from an algorithmic supervisor")

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)