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: "deep generative filter for motion deblurring"
  Abstract: Removing blur caused by camera shake in images has always been a challenging problem in computer vision literature due to its ill-posed nature. Motion blur caused due to the relative motion between the camera and the object in 3D space induces a spatially varying blurring effect over the entire image. In this paper, we propose a novel deep filter based on Generative Adversarial Network (GAN) architecture integrated with global skip connection and dense architecture in order to tackle this problem. Our model, while bypassing the process of blur kernel estimation, significantly reduces the test time which is necessary for practical applications. The experiments on the benchmark datasets prove the effectiveness of the proposed method which outperforms the state-of-the-art blind deblurring algorithms both quantitatively and qualitatively.

Connected through hyperedge HE-1 (induced by source paper "motion deblurring of faces"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "deep face deblurring"
    - "learning blind motion deblurring"
    - "motion deblurring in the wild"
    - "attention aware face hallucination via deep reinforcement learning"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "deblurgan blind motion deblurring using conditional adversarial networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "semantic image inpainting with perceptual and contextual losses"

Connected through hyperedge HE-3 (induced by source paper "robust blind deconvolution via mirror descent"):
  with members:
    - "motion deblurring in the wild"
    - "a neural approach to blind motion deblurring"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "train faster generalize better stability of stochastic gradient descent"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "learning a convolutional neural network for non uniform motion blur removal"
    - "understanding alternating minimization for matrix completion"
    - "sparse q learning with mirror descent"

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)