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: "learning joint intensity depth sparse representations"
  Abstract: This paper presents a method for learning overcomplete dictionaries composed of two modalities that describe a 3D scene: image intensity and scene depth. We propose a novel Joint Basis Pursuit (JBP) algorithm that finds related sparse features in two modalities using conic programming and integrate it into a two-step dictionary learning algorithm. JBP differs from related convex algorithms because it finds joint sparsity models with different atoms and different coefficient values for intensity and depth. This is crucial for recovering generative models where the same sparse underlying causes (3D features) give rise to different signals (intensity and depth). We give a theoretical bound for the sparse coefficient recovery error obtained by JBP, and show experimentally that JBP is far superior to the state of the art Group Lasso algorithm. When applied to the Middlebury depth-intensity database, our learning algorithm converges to a set of related features, such as pairs of depth and intensity edges or image textures and depth slants. Finally, we show that the learned dictionary and JBP achieve the state of the art depth inpainting performance on time-of-flight 3D data.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "online convolutional dictionary learning for multimodal imaging"):
  with members:
    - "depth superresolution using motion adaptive regularization"
    - "hyperspectral and multispectral image fusion based on a sparse representation"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-4 (induced by source paper "learning to remove multipath distortions in time of flight range images for a robotic arm setup"):
  with members:
    - "resolving multipath interference in time of flight imaging via modulation frequency diversity and sparse regularization"
    - "caffe convolutional architecture for fast feature embedding"
    - "sra fast removal of general multipath for tof sensors"

Connected through hyperedge HE-5 (induced by source paper "a joint intensity and depth co sparse analysis model for depth map super resolution"):
  with members:
    - "analysis operator learning and its application to image reconstruction"

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)