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: "joint blind motion deblurring and depth estimation of light field"
  Abstract: Removing camera motion blur from a single light field is a challenging task since it is highly ill-posed inverse problem. The problem becomes even worse when blur kernel varies spatially due to scene depth variation and high-order camera motion. In this paper, we propose a novel algorithm to estimate all blur model variables jointly, including latent sub-aperture image, camera motion, and scene depth from the blurred 4D light field. Exploiting multi-view nature of a light field relieves the inverse property of the optimization by utilizing strong depth cues and multi-view blur observation. The proposed joint estimation achieves high quality light field deblurring and depth estimation simultaneously under arbitrary 6-DOF camera motion and unconstrained scene depth. Intensive experiment on real and synthetic blurred light field confirms that the proposed algorithm outperforms the state-of-the-art light field deblurring and depth estimation methods.

Connected through hyperedge HE-1 (induced by source paper "learning filter basis for convolutional neural network compression"):
  with members:
    - "3d appearance super resolution with deep learning"
    - "cascaded projection end to end network compression and acceleration"
    - "amc automl for model compression and acceleration on mobile devices"
    - "efficient neural network compression"
    - "multi bin trainable linear unit for fast image restoration networks"
    - "exploiting kernel sparsity and entropy for interpretable cnn compression"
    - "extreme network compression via filter group approximation"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-2 (induced by source paper "davanet stereo deblurring with view aggregation"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "stereoscopic neural style transfer"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "pyramid stereo matching network"
    - "online video deblurring via dynamic temporal blending network"
    - "motion deblurring in the wild"
    - "deblurgan blind motion deblurring using conditional adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "single image deblurring and camera motion estimation with depth map"):
  with members:
    - "learning to extract a video sequence from a single motion blurred image"
    - "scale recurrent network for deep image deblurring"
    - "learning a discriminative prior for blind image deblurring"
    - "bringing alive blurred moments"
    - "online video deblurring via dynamic temporal blending network"
    - "self supervised learning for stereo matching with self improving ability"
    - "simultaneous stereo video deblurring and scene flow estimation"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"

Connected through hyperedge HE-4 (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"
    - "light field blind motion deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "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"

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)