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 blind motion deblurring"
  Abstract: As handheld video cameras are now commonplace and available in every smartphone, images and videos can be recorded almost everywhere at anytime. However, taking a quick shot frequently yields a blurry result due to unwanted camera shake during recording or moving objects in the scene. Removing these artifacts from the blurry recordings is a highly ill-posed problem as neither the sharp image nor the motion blur kernel is known. Propagating information between multiple consecutive blurry observations can help restore the desired sharp image or video. Solutions for blind deconvolution based on neural networks rely on a massive amount of ground-truth data which is hard to acquire. In this work, we propose an efficient approach to produce a significant amount of realistic training data and introduce a novel recurrent network architecture to deblur frames taking temporal information into account, which can efficiently handle arbitrary spatial and temporal input sizes. We demonstrate the versatility of our approach in a comprehensive comparison on a number of challening real-world examples.

Connected through hyperedge HE-1 (induced by source paper "human aware motion deblurring"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "gated fusion network for single image dehazing"
    - "scale recurrent network for deep image deblurring"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "image correction via deep reciprocating hdr transformation"
    - "deep semantic face deblurring"
    - "multi channel weighted nuclear norm minimization for real color image denoising"

Connected through hyperedge HE-2 (induced by source paper "learning deep gradient descent optimization for image deconvolution"):
  with members:
    - "mptv matching pursuit based total variation minimization for image deconvolution"
    - "online video deblurring via dynamic temporal blending network"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "learning aggregated transmission propagation networks for haze removal and beyond"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "learning to learn by gradient descent by gradient descent"
    - "learning fully convolutional networks for iterative non blind deconvolution"

Connected through hyperedge HE-3 (induced by source paper "self supervised linear motion deblurring"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "activestereonet end to end self supervised learning for active stereo systems"
    - "online video deblurring via dynamic temporal blending network"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "restoring images with unknown degradation factors by recurrent use of a multi branch network"):
  with members:
    - "dual residual networks leveraging the potential of paired operations for image restoration"
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "gated fusion network for single image dehazing"
    - "image super resolution using very deep residual channel attention networks"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "non locally enhanced encoder decoder network for single image de raining"

Connected through hyperedge HE-5 (induced by source paper "dual residual networks leveraging the potential of paired operations for image restoration"):
  with members:
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "gated fusion network for single image dehazing"
    - "yolov3 an incremental improvement"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "real world noisy image denoising a new benchmark"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "density aware single image de raining using a multi stream dense network"

Connected through hyperedge HE-6 (induced by source paper "down scaling with learned kernels in multi scale deep neural networks for non uniform single image deblurring"):
  with members:
    - "image super resolution using very deep residual channel attention networks"
    - "scale recurrent network for deep image deblurring"
    - "online video deblurring via dynamic temporal blending network"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "enhanced deep residual networks for single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "a neural approach to blind motion deblurring"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"

Connected through hyperedge HE-7 (induced by source paper "fab a robust facial landmark detection framework for motion blurred videos"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "motion deblurring of faces"
    - "style aggregated network for facial landmark detection"
    - "deep semantic face deblurring"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "online video deblurring via dynamic temporal blending network"
    - "deep face deblurring"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

Connected through hyperedge HE-8 (induced by source paper "blind motion deblurring with cycle generative adversarial networks"):
  with members:
    - "blind image deblurring using row column sparse representations"
    - "toward convolutional blind denoising of real photographs"
    - "physics based generative adversarial models for image restoration and beyond"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "learning deep cnn denoiser prior for image restoration"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "mask r cnn"

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)