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: "video deblurring via semantic segmentation and pixel wise non linear kernel"
  Abstract: Video deblurring is a challenging problem as the blur is complex and usually caused by the combination of camera shakes, object motions, and depth variations. Optical flow can be used for kernel estimation since it predicts motion trajectories. However, the estimates are often inaccurate in complex scenes at object boundaries, which are crucial in kernel estimation. In this paper, we exploit semantic segmentation in each blurry frame to understand the scene contents and use different motion models for image regions to guide optical flow estimation. While existing pixel-wise blur models assume that the blur kernel is the same as optical flow during the exposure time, this assumption does not hold when the motion blur trajectory at a pixel is different from the estimated linear optical flow. We analyze the relationship between motion blur trajectory and optical flow, and present a novel pixel-wise non-linear kernel model to account for motion blur. The proposed blur model is based on the non-linear optical flow, which describes complex motion blur more effectively. Extensive experiments on challenging blurry videos demonstrate the proposed algorithm performs favorably against the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "enlightengan deep light enhancement without paired supervision"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "joint enhancement and denoising method via sequential decomposition"
    - "getting to know low light images with the exclusively dark dataset"
    - "yolov3 an incremental improvement"
    - "pyramidbox a context assisted single shot face detector"

Connected through hyperedge HE-2 (induced by source paper "benefiting from multitask learning to improve single image super resolution"):
  with members:
    - "learn to synthesize and synthesize to learn"
    - "using photorealistic face synthesis and domain adaptation to improve facial expression analysis"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "image super resolution using very deep residual channel attention networks"
    - "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
    - "learning to maintain natural image statistics"
    - "srpgan perceptual generative adversarial network for single image super resolution"
    - "photographic image synthesis with cascaded refinement networks"

Connected through hyperedge HE-3 (induced by source paper "david dual attentional video deblurring"):
  with members:
    - "segmentation aware image denoising without knowing true segmentation"
    - "learning to optimize in swarms"
    - "deblurgan v2 deblurring orders of magnitude faster and better"
    - "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"
    - "bridging the gap between computational photography and visual recognition"
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "online video deblurring via dynamic temporal blending network"

Connected through hyperedge HE-4 (induced by source paper "spatio temporal filter adaptive network for video deblurring"):
  with members:
    - "davanet stereo deblurring with view aggregation"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "online video deblurring via dynamic temporal blending network"
    - "motion deblurring in the wild"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "video frame interpolation via adaptive convolution"

Connected through hyperedge HE-5 (induced by source paper "joint stereo video deblurring scene flow estimation and moving object segmentation"):
  with members:
    - "learning to extract a video sequence from a single motion blurred image"
    - "learning a discriminative prior for blind image deblurring"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "online video deblurring via dynamic temporal blending network"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "simultaneous stereo video deblurring and scene flow estimation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-6 (induced by source paper "adversarial spatio temporal learning for video deblurring"):
  with members:
    - "online video deblurring via dynamic temporal blending network"
    - "learning blind motion deblurring"
    - "joint estimation of camera pose depth deblurring and super resolution from a blurred image sequence"
    - "stereo video deblurring"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "deep video deblurring"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "density aware single image de raining using a multi stream dense network"):
  with members:
    - "residual dense network for image super resolution"
    - "context encoding for semantic segmentation"
    - "densely connected pyramid dehazing network"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "multi style generative network for real time transfer"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"

Connected through hyperedge HE-8 (induced by source paper "recovering realistic texture in image super resolution by deep spatial feature transform"):
  with members:
    - "modulating early visual processing by language"
    - "be your own prada fashion synthesis with structural coherence"
    - "diversified texture synthesis with feed forward networks"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "learning visual reasoning without strong priors"
    - "exploring the structure of a real time arbitrary neural artistic stylization network"
    - "film visual reasoning with a general conditioning layer"
    - "memnet a persistent memory network for image restoration"

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)