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: "recent progress in image deblurring"
  Abstract: This paper comprehensively reviews the recent development of image deblurring, including nonblind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the illposedness which is a crucial issue in deblurring tasks, existing methods can be grouped into ve categories: Bayesian inference framework, variational methods, sparse representation-based methods, homographybased modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.

Connected through hyperedge HE-1 (induced by source paper "robust online video super resolution using an efficient alternating projections scheme"):
  with members:
    - "a new adaptive video super resolution algorithm with improved robustness to innovations"
    - "super resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability"
    - "detail revealing deep video super resolution"
    - "super resolution reconstruction of electrical impedance tomography images"

Connected through hyperedge HE-2 (induced by source paper "coupled learning for facial deblur"):
  with members:
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "a comprehensive survey on pose invariant face recognition"
    - "removing camera shake via weighted fourier burst accumulation"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-3 (induced by source paper "kernel free image deblurring with a pair of blurred noisy images"):
  with members:
    - "image denoising using group sparsity residual and external nonlocal self similarity prior"
    - "image restoration and reconstruction using variable splitting and class adapted image priors"
    - "image restoration with locally selected class adapted models"

Connected through hyperedge HE-4 (induced by source paper "trunk branch ensemble convolutional neural networks for video based face recognition"):
  with members:
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "fine grained pose prediction normalization and recognition"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "robust face recognition via multimodal deep face representation"
    - "deepid3 face recognition with very deep neural networks"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-5 (induced by source paper "facial information recovery from heavily damaged images using generative adversarial network part 1"):
  with members:
    - "implementation of deep convolutional neural network in multi class categorical image classification"
    - "feed forward and backward run in deep convolution neural network"
    - "deep generative filter for motion deblurring"
    - "regularization and optimization strategies in deep convolutional neural network"
    - "hyperparameters optimization in deep convolutional neural network bayesian approach with gaussian process prior"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "improved techniques for training gans"
    - "semantic image inpainting with deep generative models"

Connected through hyperedge HE-6 (induced by source paper "ug 2 a video benchmark for assessing the impact of image restoration and enhancement on automatic visual recognition"):
  with members:
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "deep video deblurring"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "learning blind motion deblurring"):
  with members:
    - "motion deblurring in the wild"
    - "cnn for license plate motion deblurring"
    - "end to end learning for image burst deblurring"
    - "a neural approach to blind motion deblurring"
    - "dynamic scene deblurring using a locally adaptive linear blur model"
    - "deep video deblurring"
    - "spatio temporal video autoencoder with differentiable memory"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "deep video deblurring"):
  with members:
    - "stereo video deblurring"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "a neural approach to blind motion deblurring"
    - "dynamic scene deblurring using a locally adaptive linear blur model"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "context encoders feature learning by inpainting"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"

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)