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 "deep visual odometry methods for mobile robots"):
  Abstract: Technology has made navigation in 3D real time possible and this has made possible what seemed impossible. This paper explores the aspect of deep visual odometry methods for mobile robots. Visual odometry has been instrumental in making this navigation successful. Noticeable challenges in mobile robots including the inability to attain Simultaneous Localization and Mapping have been solved by visual odometry through its cameras which are suitable for human environments. More intuitive, precise and accurate detection have been made possible by visual odometry in mobile robots. Another challenge in the mobile robot world is the 3D map reconstruction for exploration. A dense map in mobile robots can facilitate for localization and more accurate findings.

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 "unsupervised odometry and depth learning for endoscopic capsule robots":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modelhub ai dissemination platform for deep learning models")
  Member "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (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-5 (induced by source paper "a survey of deep learning techniques for mobile robot applications")
  Member "direct monocular odometry using points and lines":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cubeslam monocular 3 d object slam")
  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-7 (induced by source paper "a survey of deep learning techniques for mobile robot applications")
  Member "a fully dense and globally consistent 3d map reconstruction approach for gi tract to enhance therapeutic relevance of the endoscopic capsule robot":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)