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: "depth image inpainting improving low rank matrix completion with low gradient regularization"
  Abstract: We address the task of single depth image inpainting. Without the corresponding color images, previous or next frames, depth image inpainting is quite challenging. One natural solution is to regard the image as a matrix and adopt the low rank regularization just as color image inpainting. However, the low rank assumption does not make full use of the properties of depth images. A shallow observation inspires us to penalize the nonzero gradients by sparse gradient regularization. However, statistics show that though most pixels have zero gradients, there is still a non-ignorable part of pixels, whose gradients are small but nonzero. Based on this property of depth images, we propose a low gradient regularization method in which we reduce the penalty for small gradients while penalizing the nonzero gradients to allow for gradual depth changes. The proposed low gradient regularization is integrated with the low rank regularization into the low rank low gradient approach for depth image inpainting. We compare our proposed low gradient regularization with the sparse gradient regularization. The experimental results show the effectiveness of our proposed approach.

Connected through hyperedge HE-1 (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"
    - "deep depth completion of a single rgb d image"
    - "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"

Connected through hyperedge HE-2 (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"
    - "deep depth completion of a single rgb d image"

Connected through hyperedge HE-3 (induced by source paper "image inpainting a review"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "a novel approach for robust multi human action detection and recognition based on 3 dimentional convolutional neural networks"
    - "blind visual motif removal from a single image"
    - "adversarial inpainting of medical image modalities"
    - "deep hyperspectral prior denoising inpainting super resolution"
    - "pepsi fast and lightweight network for image inpainting"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "foreground aware image inpainting"

Connected through hyperedge HE-4 (induced by source paper "learning semantics aware distance map with semantics layering network for amodal instance segmentation"):
  with members:
    - "mvtec d2s densely segmented supermarket dataset"
    - "segan segmenting and generating the invisible"
    - "mask r cnn"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "ssd single shot multibox detector"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "learning to refine object segments"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning of volume guided progressive view inpainting for 3d point scene completion from a single depth image"):
  with members:
    - "image inpainting for irregular holes using partial convolutions"
    - "deep depth completion of a single rgb d image"
    - "two stream 3d semantic scene completion"
    - "free form image inpainting with gated convolution"
    - "view volume network for semantic scene completion from a single depth image"
    - "active object reconstruction using a guided view planner"
    - "adversarial semantic scene completion from a single depth image"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"

Connected through hyperedge HE-6 (induced by source paper "depth completion from sparse lidar data with depth normal constraints"):
  with members:
    - "confidence propagation through cnns for guided sparse depth regression"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "learning depth with convolutional spatial propagation network"
    - "deep depth completion of a single rgb d image"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "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"
    - "propagating confidences through cnns for sparse data regression"

Connected through hyperedge HE-7 (induced by source paper "antipodal robotic grasping using generative residual convolutional neural network"):
  with members:
    - "learning probabilistic multi modal actor models for vision based robotic grasping"
    - "data efficient learning for sim to real robotic grasping using deep point cloud prediction networks"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "jacquard a large scale dataset for robotic grasp detection"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"

Connected through hyperedge HE-8 (induced by source paper "environment reconstruction on depth images using generative adversarial networks"):
  with members:
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "semantic road layout understanding by generative adversarial inpainting"
    - "deep depth completion of a single rgb d image"
    - "generative image inpainting with contextual attention"
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "improved training of wasserstein gans"
    - "sparsity invariant cnns"

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)