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 a convolutional neural network for non uniform motion blur removal"
  Abstract: In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motion kernels predicted by the CNN using carefully designed image rotations. A Markov random field model is then used to infer a dense non-uniform motion blur field enforcing motion smoothness. Finally, motion blur is removed by a non-uniform deblurring model using patch-level image prior. Experimental evaluations show that our approach can effectively estimate and remove complex non-uniform motion blur that is not handled well by previous approaches.

Connected through hyperedge HE-1 (induced by source paper "blur noise and compression robust generative adversarial networks"):
  with members:
    - "analyzing and improving the image quality of stylegan"
    - "noise robust generative adversarial networks"
    - "high quality self supervised deep image denoising"
    - "unsupervised domain specific deblurring via disentangled representations"
    - "noise2self blind denoising by self supervision"
    - "diversity sensitive conditional generative adversarial networks"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"

Connected through hyperedge HE-2 (induced by source paper "learning when and where to zoom with deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "efficient object detection in large images using deep reinforcement learning"
    - "learning to interpret satellite images using wikipedia"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-3 (induced by source paper "replacing mobile camera isp with a single deep learning model"):
  with members:
    - "ai benchmark all about deep learning on smartphones in 2019"
    - "aim 2019 challenge on image demoireing dataset and study"
    - "semantic image synthesis with spatially adaptive normalization"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "ai benchmark running deep neural networks on android smartphones"
    - "fast perceptual image enhancement"

Connected through hyperedge HE-4 (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-5 (induced by source paper "deblurgan v2 deblurring orders of magnitude faster and better"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "single image deraining a comprehensive benchmark analysis"
    - "enlightengan deep light enhancement without paired supervision"
    - "gated fusion network for joint image deblurring and super resolution"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the relativistic discriminator a key element missing from standard gan"
    - "scale recurrent network for deep image deblurring"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-6 (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-7 (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-8 (induced by source paper "effects of blur and deblurring to visual object tracking"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "real time mdnet"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "scale recurrent network for deep image deblurring"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "fast online object tracking and segmentation a unifying approach"
    - "vital visual tracking via adversarial learning"

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)