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 "self supervised dense 3d reconstruction from monocular endoscopic video"):
  Abstract: We present a self-supervised learning-based pipeline for dense 3D reconstruction from full-length monocular endoscopic videos without a priori modeling of anatomy or shading. Our method only relies on unlabeled monocular endoscopic videos and conventional multi-view stereo algorithms, and requires neither manual interaction nor patient CT in both training and application phases. In a cross-patient study using CT scans as groundtruth, we show that our method is able to produce photo-realistic dense 3D reconstructions with submillimeter mean residual errors from endoscopic videos from unseen patients and scopes.

Includes members:
  Member "dense depth estimation in monocular endoscopy with self supervised learning methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leveraging vision and kinematics data to improve realism of biomechanic soft tissue simulation for robotic surgery")
  Member "self supervised learning for dense depth estimation in monocular endoscopy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised scale consistent depth and ego motion learning from monocular video")
  Member "deep learning and conditional random fields based depth estimation and topographical reconstruction from conventional endoscopy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "endol2h deep super resolution for capsule endoscopy")
  Member "slam based quasi dense reconstruction for minimally invasive surgery scenes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dense depth estimation in monocular endoscopy with self supervised learning methods")

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)