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: "scale recurrent network for deep image deblurring"
  Abstract: In single image deblurring, the "coarse-to-fine" scheme, i.e. gradually restoring the sharp image on different resolutions in a pyramid, is very successful in both traditional optimization-based methods and recent neural-network-based approaches. In this paper, we investigate this strategy and propose a Scale-recurrent Network (SRN-DeblurNet) for this deblurring task. Compared with the many recent learning-based approaches in [25], it has a simpler network structure, a smaller number of parameters and is easier to train. We evaluate our method on large-scale deblurring datasets with complex motion. Results show that our method can produce better quality results than state-of-the-arts, both quantitatively and qualitatively.

Connected through hyperedge HE-1 (induced by source paper "gated fusion network for degraded image super resolution"):
  with members:
    - "gated fusion network for single image dehazing"
    - "image super resolution using very deep residual channel attention networks"
    - "yolov3 an incremental improvement"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "density aware single image de raining using a multi stream dense network"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"

Connected through hyperedge HE-2 (induced by source paper "eventsr from asynchronous events to image reconstruction restoration and super resolution via end to end adversarial learning"):
  with members:
    - "deceiving image to image translation networks for autonomous driving with adversarial perturbations"
    - "events to video bringing modern computer vision to event cameras"
    - "end to end learning of representations for asynchronous event based data"
    - "a deep journey into super resolution a survey"
    - "focus is all you need loss functions for event based vision"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "event based moving object detection and tracking"
    - "the relativistic discriminator a key element missing from standard gan"

Connected through hyperedge HE-3 (induced by source paper "blurry video frame interpolation"):
  with members:
    - "unsupervised video interpolation using cycle consistency"
    - "depth aware video frame interpolation"
    - "feedback network for image super resolution"
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "adversarial spatio temporal learning for video deblurring"
    - "temporal cycle consistency learning"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "video enhancement with task oriented flow"

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"
    - "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"
    - "learning blind motion deblurring"

Connected through hyperedge HE-5 (induced by source paper "deep video super resolution using hr optical flow estimation"):
  with members:
    - "frame recurrent video super resolution"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "learning for video super resolution through hr optical flow estimation"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "pyramid stereo matching network"
    - "deep laplacian pyramid networks for fast and accurate super resolution"

Connected through hyperedge HE-6 (induced by source paper "self supervised linear motion deblurring"):
  with members:
    - "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"
    - "learning blind motion deblurring"
    - "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-7 (induced by source paper "handling noise in image deblurring via joint learning"):
  with members:
    - "efficient blind deblurring under high noise levels"
    - "gated fusion network for joint image deblurring and super resolution"
    - "learning to deblur images with exemplars"
    - "toward convolutional blind denoising of real photographs"
    - "deep semantic face deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"

Connected through hyperedge HE-8 (induced by source paper "automatic catheter detection in pediatric x ray images using a scale recurrent network and synthetic data"):
  with members:
    - "sharpness aware low dose ct denoising using conditional generative adversarial network"
    - "fully automatic and real time catheter segmentation in x ray fluoroscopy"
    - "scan structure correcting adversarial network for organ segmentation in chest x rays"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "fully convolutional networks for semantic segmentation"
    - "retinal vessel segmentation using the 2 d gabor wavelet and supervised classification"

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)