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: "deep depth completion of a single rgb d image"
  Abstract: The goal of our work is to complete the depth channel of an RGB-D image. Commodity-grade depth cameras often fail to sense depth for shiny, bright, transparent, and distant surfaces. To address this problem, we train a deep network that takes an RGB image as input and predicts dense surface normals and occlusion boundaries. Those predictions are then combined with raw depth observations provided by the RGB-D camera to solve for depths for all pixels, including those missing in the original observation. This method was chosen over others (e.g., inpainting depths directly) as the result of extensive experiments with a new depth completion benchmark dataset, where holes are filled in training data through the rendering of surface reconstructions created from multiview RGB-D scans. Experiments with different network inputs, depth representations, loss functions, optimization methods, inpainting methods, and deep depth estimation networks show that our proposed approach provides better depth completions than these alternatives.

Connected through hyperedge HE-1 (induced by source paper "depth estimation by learning triangulation and densification of sparse points for multi view stereo"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "from big to small multi scale local planar guidance for monocular depth estimation"
    - "multi view stereo by temporal nonparametric fusion"
    - "learnable triangulation of human pose"
    - "gradslam dense slam meets automatic differentiation"
    - "superglue learning feature matching with graph neural networks"
    - "efficientdet scalable and efficient object detection"
    - "dpsnet end to end deep plane sweep stereo"

Connected through hyperedge HE-2 (induced by source paper "unsupervised enhancement of real world depth images using tri cycle gan"):
  with members:
    - "s flow gan"
    - "rethinking lossy compression the rate distortion perception tradeoff"
    - "generative adversarial networks for extreme learned image compression"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"
    - "depth map completion by jointly exploiting blurry color images and sparse depth maps"
    - "sparse to dense depth prediction from sparse depth samples and a single image"

Connected through hyperedge HE-3 (induced by source paper "fast generation of high fidelity rgb d images by deep learning with adaptive convolution"):
  with members:
    - "residual dense network for image super resolution"
    - "pu net point cloud upsampling network"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "conditional image generation with pixelcnn decoders"
    - "deeper depth prediction with fully convolutional residual networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-4 (induced by source paper "learning to correct 3d reconstructions from multiple views"):
  with members:
    - "learning geometrically consistent mesh corrections"
    - "group normalization"
    - "meshed up learnt error correction in 3d reconstructions"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "deeper depth prediction with fully convolutional residual networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "dynamic filter networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "learning a discriminative feature network for semantic segmentation"
    - "dynamic video segmentation network"
    - "low latency video semantic segmentation"
    - "generative image inpainting with contextual attention"
    - "skip connections eliminate singularities"
    - "cycada cycle consistent adversarial domain adaptation"
    - "flow guided feature aggregation for video object detection"

Connected through hyperedge HE-6 (induced by source paper "cam convs camera aware multi scale convolutions for single view depth"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deeptam deep tracking and mapping"
    - "deep attention based classification network for robust depth prediction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "deep ordinal regression network for monocular depth estimation"
    - "deepmvs learning multi view stereopsis"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

Connected through hyperedge HE-7 (induced by source paper "depth completion via deep basis fitting"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "depth coefficients for depth completion"
    - "dense depth posterior ddp from single image and sparse range"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "voiced depth completion from inertial odometry and vision"
    - "noise aware unsupervised deep lidar stereo fusion"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"

Connected through hyperedge HE-8 (induced by source paper "to complete or to estimate that is the question a multi task approach to depth completion and monocular depth estimation"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "geometry aware symmetric domain adaptation for monocular depth estimation"
    - "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"

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)