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: "epinet a fully convolutional neural network using epipolar geometry for depth from light field images"
  Abstract: Light field cameras capture both the spatial and the angular properties of light rays in space. Due to its property, one can compute the depth from light fields in uncontrolled lighting environments, which is a big advantage over active sensing devices. Depth computed from light fields can be used for many applications including 3D modelling and refocusing. However, light field images from hand-held cameras have very narrow baselines with noise, making the depth estimation difficult. any approaches have been proposed to overcome these limitations for the light field depth estimation, but there is a clear trade-off between the accuracy and the speed in these methods. In this paper, we introduce a fast and accurate light field depth estimation method based on a fully-convolutional neural network. Our network is designed by considering the light field geometry and we also overcome the lack of training data by proposing light field specific data augmentation methods. We achieved the top rank in the HCI 4D Light Field Benchmark on most metrics, and we also demonstrate the effectiveness of the proposed method on real-world light-field images.

Connected through hyperedge HE-1 (induced by source paper "5d light field synthesis from a monocular video"):
  with members:
    - "synthesizing a 4d spatio angular consistent light field from a single image"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "pushing the boundaries of view extrapolation with multiplane images"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"
    - "learning blind video temporal consistency"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "stereo magnification learning view synthesis using multiplane images"

Connected through hyperedge HE-2 (induced by source paper "light field saliency detection with deep convolutional networks"):
  with members:
    - "salient object detection by lossless feature reflection"
    - "a double deep spatio angular learning framework for light field based face recognition"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep contrast learning for salient object detection"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "learning based view synthesis for light field cameras"

Connected through hyperedge HE-3 (induced by source paper "stratified labeling for surface consistent parallax correction and occlusion completion"):
  with members:
    - "accurate light field depth estimation with superpixel regularization over partially occluded regions"
    - "stereo magnification learning view synthesis using multiplane images"
    - "light field video capture using a learning based hybrid imaging system"
    - "towards a quality metric for dense light fields"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "semantic image inpainting with deep generative models"
    - "learning based view synthesis for light field cameras"

Connected through hyperedge HE-4 (induced by source paper "learning to think outside the box wide baseline light field depth estimation with epi shift"):
  with members:
    - "dpsnet end to end deep plane sweep stereo"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "deep ordinal regression network for monocular depth estimation"
    - "learning monocular depth by distilling cross domain stereo networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "deoccnet learning to see through foreground occlusions in light fields"):
  with members:
    - "generative image inpainting with contextual attention"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "context encoders feature learning by inpainting"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "rapid light field depth estimation with semi global matching"):
  with members:
    - "a compact light field camera for real time depth estimation"
    - "fast and efficient depth map estimation from light fields"
    - "embedded real time stereo estimation via semi global matching on the gpu"

Connected through hyperedge HE-7 (induced by source paper "a compact light field camera for real time depth estimation"):
  with members:
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "fast and efficient depth map estimation from light fields"

Connected through hyperedge HE-8 (induced by source paper "a unified learning based framework for light field reconstruction from coded projections"):
  with members:
    - "learning light field reconstruction from a single coded image"
    - "spatial and angular resolution enhancement of light fields using convolutional neural networks"
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "aperture supervision for monocular depth estimation"
    - "unsupervised monocular depth estimation with left right consistency"
    - "learning based view synthesis for light field cameras"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "partial light field tomographic reconstruction from a fixed camera focal stack"

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)