human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "self supervised learning for dense depth estimation in monocular endoscopy"
  Abstract: We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading. Our method only requires sequential data from monocular endoscopic videos and a multi-view stereo reconstruction method, e.g. structure from motion, that supervises learning in a sparse but accurate manner. Consequently, our method requires neither manual interaction, such as scaling or labeling, nor patient CT in the training and application phases. We demonstrate the performance of our method on sinus endoscopy data from two patients and validate depth prediction quantitatively using corresponding patient CT scans where we found submillimeter residual errors. (Link to the supplementary video: https://camp.lcsr.jhu.edu/miccai-2018-demonstration-videos/)

Connected through hyperedge HE-1 (induced by source paper "unsupervised scale consistent depth and ego motion learning from monocular video"):
  with members:
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "joint unsupervised learning of optical flow and depth by watching stereo videos"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "hierarchical discrete distribution decomposition for match density estimation"

Connected through hyperedge HE-2 (induced by source paper "dense depth estimation in monocular endoscopy with self supervised learning methods"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "unsupervised learning of depth and ego motion from video"
    - "deep learning and conditional random fields based depth estimation and topographical reconstruction from conventional endoscopy"
    - "slam based quasi dense reconstruction for minimally invasive surgery scenes"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "self supervised dense 3d reconstruction from monocular endoscopic video"):
  with members:
    - "dense depth estimation in monocular endoscopy with self supervised learning methods"
    - "deep learning and conditional random fields based depth estimation and topographical reconstruction from conventional endoscopy"
    - "slam based quasi dense reconstruction for minimally invasive surgery scenes"

Question: Please tell me which class the center node 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)