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: "light field blind motion deblurring"
  Abstract: We study the problem of deblurring light fields of general 3D scenes captured under 3D camera motion and present both theoretical and practical contributions. By analyzing the motion-blurred light field in the primal and Fourier domains, we develop intuition into the effects of camera motion on the light field, show the advantages of capturing a 4D light field instead of a conventional 2D image for motion deblurring, and derive simple methods of motion deblurring in certain cases. We then present an algorithm to blindly deblur light fields of general scenes without any estimation of scene geometry, and demonstrate that we can recover both the sharp light field and the 3D camera motion path of real and synthetically-blurred light fields.

Connected through hyperedge HE-1 (induced by source paper "learning high fidelity light field images from hybrid inputs"):
  with members:
    - "deep learning for image super resolution a survey"
    - "geometry consistent light field super resolution via graph based regularization"
    - "residual dense network for image super resolution"
    - "crossnet an end to end reference based super resolution network using cross scale warping"
    - "light field denoising via anisotropic parallax analysis in a cnn framework"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "a 4d light field dataset and cnn architectures for material recognition"

Connected through hyperedge HE-2 (induced by source paper "deep recurrent network for fast and full resolution light field deblurring"):
  with members:
    - "online video deblurring via dynamic temporal blending network"
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "joint blind motion deblurring and depth estimation of light field"
    - "instance normalization the missing ingredient for fast stylization"
    - "richardson lucy deblurring for moving light field cameras"
    - "learning based view synthesis for light field cameras"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"

Connected through hyperedge HE-3 (induced by source paper "liff light field features in scale and depth"):
  with members:
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "transcut transparent object segmentation from a light field image"
    - "learning rich features from rgb d images for object detection and segmentation"
    - "domain size pooling in local descriptors dsp sift"

Connected through hyperedge HE-4 (induced by source paper "joint blind motion deblurring and depth estimation of light field"):
  with members:
    - "joint estimation of camera pose depth deblurring and super resolution from a blurred image sequence"
    - "richardson lucy deblurring for moving light field cameras"
    - "stereo video deblurring"
    - "generalized video deblurring for dynamic scenes"
    - "learning a convolutional neural network for non uniform motion blur removal"

Connected through hyperedge HE-5 (induced by source paper "motion deblurring for light fields"):
  with members:
    - "motion deblurring for plenoptic images"

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)