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: "joint image filtering with deep convolutional networks"
  Abstract: Joint image filters leverage the guidance image as a prior and transfer the structural details from the guidance image to the target image for suppressing noise or enhancing spatial resolution. Existing methods either rely on various explicit filter constructions or hand-designed objective functions, thereby making it difficult to understand, improve, and accelerate these filters in a coherent framework. In this paper, we propose a learning-based approach for constructing joint filters based on Convolutional Neural Networks. In contrast to existing methods that consider only the guidance image, the proposed algorithm can selectively transfer salient structures that are consistent with both guidance and target images. We show that the model trained on a certain type of data, e.g., RGB and depth images, generalizes well to other modalities, e.g., flash/non-Flash and RGB/NIR images. We validate the effectiveness of the proposed joint filter through extensive experimental evaluations with state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "concurrently extrapolating and interpolating networks for continuous model generation"):
  with members:
    - "bi directional cascade network for perceptual edge detection"
    - "cfsnet toward a controllable feature space for image restoration"
    - "pixel adaptive convolutional neural networks"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "image super resolution by neural texture transfer"
    - "a general decoupled learning framework for parameterized image operators"
    - "a benchmark for edge preserving image smoothing"

Connected through hyperedge HE-2 (induced by source paper "learning task specific generalized convolutions in the permutohedral lattice"):
  with members:
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic filtering with large sampling field for convnets"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "fast end to end trainable guided filter"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "sniper efficient multi scale training"
    - "segmentation aware convolutional networks using local attention masks"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-3 (induced by source paper "pixel adaptive convolutional neural networks"):
  with members:
    - "image transformer"
    - "convolutional crfs for semantic segmentation"
    - "splatnet sparse lattice networks for point cloud processing"
    - "fast end to end trainable guided filter"
    - "segmentation aware convolutional networks using local attention masks"
    - "non local neural networks"
    - "attention is all you need"
    - "learning affinity via spatial propagation networks"

Connected through hyperedge HE-4 (induced by source paper "deformable kernel networks for joint image filtering"):
  with members:
    - "pixel adaptive convolutional neural networks"
    - "learning guided convolutional network for depth completion"
    - "blade filter learning for general purpose computational photography"
    - "video frame interpolation via adaptive convolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "burst denoising with kernel prediction networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-5 (induced by source paper "guided image to image translation with bi directional feature transformation"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "multimodal unsupervised image to image translation"
    - "learning blind video temporal consistency"
    - "free form image inpainting with gated convolution"
    - "dense pose transfer"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "drit diverse image to image translation via disentangled representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-6 (induced by source paper "self supervised deep depth denoising"):
  with members:
    - "noise2self blind denoising by self supervision"
    - "a low cost flexible and portable volumetric capturing system"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "noise2void learning denoising from single noisy images"
    - "layer structured 3d scene inference via view synthesis"
    - "robust intrinsic and extrinsic calibration of rgb d cameras"
    - "tackling 3d tof artifacts through learning and the flat dataset"
    - "deeptof off the shelf real time correction of multipath interference in time of flight imaging"

Connected through hyperedge HE-7 (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-8 (induced by source paper "deformable kernel networks for guided depth map upsampling"):
  with members:
    - "fast end to end trainable guided filter"
    - "video frame interpolation via adaptive convolution"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "accurate image super resolution using very deep convolutional networks"
    - "conditional random fields as recurrent neural networks"
    - "deep residual learning for image recognition"
    - "fully convolutional networks for semantic segmentation"
    - "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)