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 a surgical perception framework for endoscopic tissue manipulation with surgical robotics"):
  Abstract: Traditional control and task automation have been successfully demonstrated in a variety of structured, controlled environments through the use of highly specialized modeled robotic systems in conjunction with multiple sensors. However, application of autonomy in endoscopic surgery is very challenging, particularly in soft tissue work, due to the lack of high-quality images and the unpredictable, constantly deforming environment. In this work, we propose a novel surgical perception framework, SuPer, for surgical robotic control. This framework continuously collects 3D geometric information that allows for mapping of a deformable surgical field while tracking rigid instruments within the field. To achieve this, a model-based tracker is employed to localize the surgical tool with a kinematic prior in conjunction with a model-free tracker to reconstruct the deformable environment and provide an estimated point cloud as a mapping of the environment. The proposed framework was implemented on the da Vinci Surgical System in real-time with an end-effector controller where the target configurations are set and regulated through the framework. Our proposed framework successfully completed autonomous soft tissue manipulation tasks with high accuracy. The demonstration of this novel framework is promising for the future of surgical autonomy. In addition, we provide our dataset for further surgical research.

Includes members:
  Member "surfelwarp efficient non volumetric single view dynamic reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "super deep a surgical perception framework for robotic tissue manipulation using deep learning for feature extraction")
  Member "open sourced reinforcement learning environments for surgical robotics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "super deep a surgical perception framework for robotic tissue manipulation using deep learning for feature extraction")
  Member "augmented reality predictive displays to help mitigate the effects of delayed telesurgery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "super deep a surgical perception framework for robotic tissue manipulation using deep learning for feature extraction")
  Member "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep imitation learning of sequential fabric smoothing from an algorithmic supervisor")
  Member "robust estimation of similarity transformation for visual object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "super deep a surgical perception framework for robotic tissue manipulation using deep learning for feature extraction")
  Member "robot autonomy for surgery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "super deep a surgical perception framework for robotic tissue manipulation using deep learning for feature extraction")
  Member "volumedeform real time volumetric non rigid reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepcap monocular human performance capture using weak supervision")
  Member "real time 3d tracking of articulated tools for robotic surgery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "super deep a surgical perception framework for robotic tissue manipulation using deep learning for feature extraction")

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)