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: "cnn for license plate motion deblurring"
  Abstract: In this work we explore the previously proposed approach of direct blind deconvolution and denoising with convolutional neural networks in a situation where the blur kernels are partially constrained. We focus on blurred images from a real-life traffic surveillance system, on which we, for the first time, demonstrate that neural networks trained on artificial data provide superior reconstruction quality on real images compared to traditional blind deconvolution methods. The training data is easy to obtain by blurring sharp photos from a target system with a very rough approximation of the expected blur kernels, thereby allowing custom CNNs to be trained for a specific application (image content and blur range). Additionally, we evaluate the behavior and limits of the CNNs with respect to blur direction range and length.

Connected through hyperedge HE-1 (induced by source paper "deblurring face images using uncertainty guided multi stream semantic networks"):
  with members:
    - "joint face hallucination and deblurring via structure generation and detail enhancement"
    - "deep semantic face deblurring"
    - "smoothed dilated convolutions for improved dense prediction"
    - "multi style generative network for real time transfer"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "densely connected convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "an efficient and layout independent automatic license plate recognition system based on the yolo detector"):
  with members:
    - "convolutional neural networks for automatic meter reading"
    - "yolov3 an incremental improvement"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "license plate detection and recognition using deeply learned convolutional neural networks"
    - "focal loss for dense object detection"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"

Connected through hyperedge HE-3 (induced by source paper "a robust real time automatic license plate recognition based on the yolo detector"):
  with members:
    - "license plate detection and recognition using deeply learned convolutional neural networks"
    - "towards end to end car license plates detection and recognition with deep neural networks"
    - "yolo9000 better faster stronger"
    - "reading car license plates using deep convolutional neural networks and lstms"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"
    - "benchmark for license plate character segmentation"
    - "a large scale car dataset for fine grained categorization and verification"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-4 (induced by source paper "learning blind motion deblurring"):
  with members:
    - "motion deblurring in the wild"
    - "end to end learning for image burst deblurring"
    - "a neural approach to blind motion deblurring"
    - "dynamic scene deblurring using a locally adaptive linear blur model"
    - "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-5 (induced by source paper "motion deblurring in the wild"):
  with members:
    - "parametric object motion from blur"
    - "end to end learning for image burst deblurring"
    - "a neural approach to blind motion deblurring"
    - "dynamic scene deblurring using a locally adaptive linear blur model"
    - "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 "compression artifacts removal using convolutional neural networks"):
  with members:
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "compression artifacts reduction by a deep convolutional network"
    - "learning a convolutional neural network for non uniform motion blur removal"
    - "learning to deblur"
    - "show and tell a neural image caption generator"
    - "deeppose human pose estimation via deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "end to end learning for image burst deblurring"):
  with members:
    - "a neural approach to blind motion deblurring"
    - "dynamic scene deblurring using a locally adaptive linear blur model"
    - "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)