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: "a joint intensity and depth co sparse analysis model for depth map super resolution"
  Abstract: High-resolution depth maps can be inferred from low-resolution depth measurements and an additional high-resolution intensity image of the same scene. To that end, we introduce a bimodal co-sparse analysis model, which is able to capture the interdependency of registered intensity and depth information. This model is based on the assumption that the co-supports of corresponding bimodal image structures are aligned when computed by a suitable pair of analysis operators. No analytic form of such operators exist and we propose a method for learning them from a set of registered training signals. This learning process is done offline and returns a bimodal analysis operator that is universally applicable to natural scenes. We use this to exploit the bimodal co-sparse analysis model as a prior for solving inverse problems, which leads to an efficient algorithm for depth map super-resolution.

Connected through hyperedge HE-1 (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-2 (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"
    - "deep depth super resolution learning depth super resolution using deep convolutional neural 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"

Connected through hyperedge HE-3 (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-4 (induced by source paper "pag net progressive attention guided depth super resolution network"):
  with members:
    - "deeply supervised depth map super resolution as novel view synthesis"
    - "residual dense network for image super resolution"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"):
  with members:
    - "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"
    - "free form image inpainting with gated convolution"
    - "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"
    - "lego learning edge with geometry all at once by watching videos"

Connected through hyperedge HE-6 (induced by source paper "deep depth completion of a single rgb d image"):
  with members:
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "real time user guided image colorization with learned deep priors"
    - "matterport3d learning from rgb d data in indoor environments"
    - "depth image inpainting improving low rank matrix completion with low gradient regularization"
    - "sparsity invariant cnns"
    - "semantic scene completion from a single depth image"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "model based learning of local image features for unsupervised texture segmentation"):
  with members:
    - "variational multi phase segmentation using high dimensional local features"
    - "a bimodal co sparse analysis model for image processing"
    - "fully convolutional networks for semantic segmentation"
    - "analysis operator learning and its application to image reconstruction"
    - "segmentation of natural images by texture and boundary compression"

Connected through hyperedge HE-8 (induced by source paper "semantic aware depth super resolution in outdoor scenes"):
  with members:
    - "insights into analysis operator learning from patch based sparse models to higher order mrfs"
    - "analysis operator learning and its application to image reconstruction"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

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)