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 super resolution learning depth super resolution using deep convolutional neural network"
  Abstract: Depth image super-resolution is an extremely challenging task due to the information loss in sub-sampling. Deep convolutional neural network have been widely applied to color image super-resolution. Quite surprisingly, this success has not been matched to depth super-resolution. This is mainly due to the inherent difference between color and depth images. In this paper, we bridge up the gap and extend the success of deep convolutional neural network to depth super-resolution. The proposed deep depth super-resolution method learns the mapping from a low-resolution depth image to a high resolution one in an end-to-end style. Furthermore, to better regularize the learned depth map, we propose to exploit the depth field statistics and the local correlation between depth image and color image. These priors are integrated in an energy minimization formulation, where the deep neural network learns the unary term, the depth field statistics works as global model constraint and the color-depth correlation is utilized to enforce the local structure in depth images. Extensive experiments on various depth super-resolution benchmark datasets show that our method outperforms the state-of-the-art depth image super-resolution methods with a margin.

Connected through hyperedge HE-1 (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-2 (induced by source paper "conf net toward high confidence dense 3d point cloud with error map prediction"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "generating and exploiting probabilistic monocular depth estimates"
    - "detail preserving depth estimation from a single image using attention guided networks"
    - "deep ordinal regression network for monocular depth estimation"
    - "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-3 (induced by source paper "conf net predicting depth completion error map for high confidence dense 3d point cloud"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "generating and exploiting probabilistic monocular depth estimates"
    - "detail preserving depth estimation from a single image using attention guided networks"
    - "deep ordinal regression network for monocular depth estimation"
    - "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-4 (induced by source paper "deeply supervised depth map super resolution as novel view synthesis"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"
    - "simultaneous super resolution and cross modality synthesis of 3d medical images using weakly supervised joint convolutional sparse coding"
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "a deep primal dual network for guided depth super resolution"
    - "enhancenet single image super resolution through automated texture synthesis"

Connected through hyperedge HE-5 (induced by source paper "deep convolutional neural network for multi modal image restoration and fusion"):
  with members:
    - "densefuse a fusion approach to infrared and visible images"
    - "feedback network for image super resolution"
    - "deformable kernel networks for joint image filtering"
    - "fast end to end trainable guided filter"
    - "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"
    - "co occurrence filter"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"

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"
    - "a deep primal dual network for guided depth super resolution"

Connected through hyperedge HE-7 (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"

Connected through hyperedge HE-8 (induced by source paper "multimedia super resolution via deep learning a survey"):
  with members:
    - "joint convolutional neural pyramid for depth map super resolution"
    - "learning a mixture of deep networks for single image super resolution"
    - "an analysis of deep neural network models for practical applications"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "failures of gradient based deep learning"
    - "enhanced deep residual networks for single image super resolution"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "single image super resolution when model adaptation matters"

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)