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: "atgv net accurate depth super resolution"
  Abstract: In this work we present a novel approach for single depth map super-resolution. Modern consumer depth sensors, especially Time-of-Flight sensors, produce dense depth measurements, but are affected by noise and have a low lateral resolution. We propose a method that combines the benefits of recent advances in machine learning based single image super-resolution, i.e. deep convolutional networks, with a variational method to recover accurate high-resolution depth maps. In particular, we integrate a variational method that models the piecewise affine structures apparent in depth data via an anisotropic total generalized variation regularization term on top of a deep network. We call our method ATGV-Net and train it end-to-end by unrolling the optimization procedure of the variational method. To train deep networks, a large corpus of training data with accurate ground-truth is required. We demonstrate that it is feasible to train our method solely on synthetic data that we generate in large quantities for this task. Our evaluations show that we achieve state-of-the-art results on three different benchmarks, as well as on a challenging Time-of-Flight dataset, all without utilizing an additional intensity image as guidance.

Connected through hyperedge HE-1 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems"):
  with members:
    - "hierarchical deep stereo matching on high resolution images"
    - "ga net guided aggregation net for end to end stereo matching"
    - "learned collaborative stereo refinement"
    - "learning depth with convolutional spatial propagation network"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"

Connected through hyperedge HE-2 (induced by source paper "learned multi view texture super resolution"):
  with members:
    - "3d appearance super resolution with deep learning"
    - "deep learning for single image super resolution a brief review"
    - "fast and accurate single image super resolution via information distillation network"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimedia super resolution via deep learning a survey"
    - "a fully progressive approach to single image super resolution"
    - "residual dense network for image super resolution"
    - "deep network interpolation for continuous imagery effect transition"

Connected through hyperedge HE-3 (induced by source paper "revealing scenes by inverting structure from motion reconstructions"):
  with members:
    - "privacy preserving image based localization"
    - "learning privacy preserving encodings through adversarial training"
    - "deep multi scale architectures for monocular depth estimation"
    - "semi parametric image synthesis"
    - "megadepth learning single view depth prediction from internet photos"
    - "image manipulation with perceptual discriminators"
    - "estimating depth from rgb and sparse sensing"
    - "smart sparse contours to represent and edit images"

Connected through hyperedge HE-4 (induced by source paper "sparse to continuous enhancing monocular depth estimation using occupancy maps"):
  with members:
    - "from big to small multi scale local planar guidance for monocular depth estimation"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "j mod 2 joint monocular obstacle detection and depth estimation"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "unsupervised learning of depth and ego motion from video"
    - "sparsity invariant cnns"

Connected through hyperedge HE-5 (induced by source paper "learned collaborative stereo refinement"):
  with members:
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "learning energy based inpainting for optical flow"
    - "pyramid stereo matching network"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "learning for disparity estimation through feature constancy"
    - "detect replace refine deep structured prediction for pixel wise labeling"
    - "solving dense image matching in real time using discrete continuous optimization"
    - "end to end training of hybrid cnn crf models for stereo"

Connected through hyperedge HE-6 (induced by source paper "inferring super resolution depth from a moving light source enhanced rgb d sensor a variational approach"):
  with members:
    - "on the well posedness of uncalibrated photometric stereo under general lighting"
    - "photometric depth super resolution"
    - "a differential volumetric approach to multi view photometric stereo"
    - "detailed surface geometry and albedo recovery from rgb d video under natural illumination"
    - "led based photometric stereo modeling calibration and numerical solution"
    - "normal integration a survey"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep depth super resolution learning depth super resolution using deep convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "indoor depth completion with boundary consistency and self attention"):
  with members:
    - "deep depth completion of a single rgb d image"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "free form image inpainting with gated convolution"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "matterport3d learning from rgb d data in indoor environments"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "deeper depth prediction with fully convolutional residual networks"

Connected through hyperedge HE-8 (induced by source paper "learning depth with convolutional spatial propagation network"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "single view stereo matching"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"

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)