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: "dynamic scene deblurring using a locally adaptive linear blur model"
  Abstract: State-of-the-art video deblurring methods cannot handle blurry videos recorded in dynamic scenes, since they are built under a strong assumption that the captured scenes are static. Contrary to the existing methods, we propose a video deblurring algorithm that can deal with general blurs inherent in dynamic scenes. To handle general and locally varying blurs caused by various sources, such as moving objects, camera shake, depth variation, and defocus, we estimate pixel-wise non-uniform blur kernels. We infer bidirectional optical flows to handle motion blurs, and also estimate Gaussian blur maps to remove optical blur from defocus in our new blur model. Therefore, we propose a single energy model that jointly estimates optical flows, defocus blur maps and latent frames. We also provide a framework and efficient solvers to minimize the proposed energy model. By optimizing the energy model, we achieve significant improvements in removing general blurs, estimating optical flows, and extending depth-of-field in blurry frames. Moreover, in this work, to evaluate the performance of non-uniform deblurring methods objectively, we have constructed a new realistic dataset with ground truths. In addition, extensive experimental on publicly available challenging video data demonstrate that the proposed method produces qualitatively superior performance than the state-of-the-art methods which often fail in either deblurring or optical flow estimation.

Connected through hyperedge HE-1 (induced by source paper "gan based image deblurring using dark channel prior"):
  with members:
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "a neural approach to blind motion deblurring"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "learning to extract a video sequence from a single motion blurred image"):
  with members:
    - "online video deblurring via dynamic temporal blending network"
    - "learning blind motion deblurring"
    - "motion deblurring in the wild"
    - "simultaneous stereo video deblurring and scene flow estimation"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"
    - "joint estimation of camera pose depth deblurring and super resolution from a blurred image sequence"
    - "perceptual losses for real time style transfer and super resolution"
    - "stereo video deblurring"

Connected through hyperedge HE-3 (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"
    - "deep video deblurring"
    - "spatio temporal video autoencoder with differentiable memory"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "online video deblurring via dynamic temporal blending network"):
  with members:
    - "stereo video deblurring"
    - "identity mappings in deep residual networks"
    - "a neural approach to blind motion deblurring"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "deep video deblurring"
    - "dynamic filter networks"
    - "enhancenet single image super resolution through automated texture synthesis"
    - "spatial transformer networks"

Connected through hyperedge HE-5 (induced by source paper "motion deblurring in the wild"):
  with members:
    - "cnn for license plate motion deblurring"
    - "parametric object motion from blur"
    - "end to end learning for image burst deblurring"
    - "a neural approach to blind motion deblurring"
    - "flownet learning optical flow with convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "generalized video deblurring for dynamic scenes"
    - "learning a convolutional neural network for non uniform motion blur removal"

Connected through hyperedge HE-6 (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"
    - "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"
    - "flownet learning optical flow with convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "deep multi scale convolutional neural network for dynamic scene deblurring"):
  with members:
    - "a neural approach to blind motion deblurring"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "flownet learning optical flow with convolutional networks"
    - "deep residual learning for image recognition"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "generalized video deblurring for dynamic scenes"
    - "learning a convolutional neural network for non uniform motion blur removal"
    - "deep multi scale video prediction beyond mean square error"

Connected through hyperedge HE-8 (induced by source paper "end to end learning for image burst deblurring"):
  with members:
    - "cnn for license plate motion deblurring"
    - "a neural approach to blind motion deblurring"
    - "spatio temporal video autoencoder with differentiable memory"
    - "spatial transformer networks"
    - "hand held video deblurring via efficient fourier aggregation"
    - "learning a convolutional neural network for non uniform motion blur removal"
    - "adam a method for stochastic optimization"
    - "recent progress in image deblurring"

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)