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 "magnetically guided capsule endoscopy"):
  Abstract: The following research undertakes a historical review of this technology with specific highlighting of its advancement in medical diagnostics as well as the therapeutic functionality of wireless capsule endoscopy. Without restriction to the gastrointestinal tract alone, the review will additionally investigate the developments in the technology of micro-robots guided through the magnetic power and are capable of navigating through multiple forms of air and fluid filled lumina as well as cavities within the body. All these capabilities are of use in the utilization of minimally invasive medicine.

Includes members:
  Member "sparse then dense alignment based 3d map reconstruction method for endoscopic capsule robots":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reconstructing sinus anatomy from endoscopic video towards a radiation free approach for quantitative longitudinal assessment")
  Member "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "endol2h deep super resolution for capsule endoscopy")
  Member "magnetic visual sensor fusion based dense 3d reconstruction and localization for endoscopic capsule robots":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey of deep learning techniques for mobile robot applications")
  Member "endo vmfusenet deep visual magnetic sensor fusion approach for uncalibrated unsynchronized and asymmetric endoscopic capsule robot localization data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey of deep learning techniques for mobile robot applications")
  Member "endosensorfusion particle filtering based multi sensory data fusion with switching state space model for endoscopic capsule robots":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey of deep learning techniques for mobile robot applications")
  Member "a deep learning based 6 degree of freedom localization method for endoscopic capsule robots":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey of deep learning techniques for mobile robot applications")

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.CV(Computer Vision and Pattern Recognition)