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 "unsupervised odometry and depth learning for endoscopic capsule robots"):
  Abstract: In the last decade, many medical companies and research groups have tried to convert passive capsule endoscopes as an emerging and minimally invasive diagnostic technology into actively steerable endoscopic capsule robots which will provide more intuitive disease detection, targeted drug delivery and biopsy-like operations in the gastrointestinal(GI) tract. In this study, we introduce a fully unsupervised, real-time odometry and depth learner for monocular endoscopic capsule robots. We establish the supervision by warping view sequences and assigning the re-projection minimization to the loss function, which we adopt in multi-view pose estimation and single-view depth estimation network. Detailed quantitative and qualitative analyses of the proposed framework performed on non-rigidly deformable ex-vivo porcine stomach datasets proves the effectiveness of the method in terms of motion estimation and depth recovery.

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 "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-3 (induced by source paper "a survey of deep learning techniques for mobile robot applications")
  Member "unsupervised learning of depth and ego motion from video":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  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 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-6 (induced by source paper "a survey of deep learning techniques for mobile robot applications")
  Member "a non rigid map fusion based direct slam method for endoscopic capsule robots":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "structured coupled generative adversarial networks for unsupervised monocular depth estimation")
  Member "six degree of freedom localization of endoscopic capsule robots using recurrent neural networks embedded into a convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots")

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)