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 video deblurring"
  Abstract: Motion blur from camera shake is a major problem in videos captured by hand-held devices. Unlike single-image deblurring, video-based approaches can take advantage of the abundant information that exists across neighboring frames. As a result the best performing methods rely on aligning nearby frames. However, aligning images is a computationally expensive and fragile procedure, and methods that aggregate information must therefore be able to identify which regions have been accurately aligned and which have not, a task which requires high level scene understanding. In this work, we introduce a deep learning solution to video deblurring, where a CNN is trained end-to-end to learn how to accumulate information across frames. To train this network, we collected a dataset of real videos recorded with a high framerate camera, which we use to generate synthetic motion blur for supervision. We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.

Connected through hyperedge HE-1 (induced by source paper "bridging the gap between computational photography and visual recognition"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "isaid a large scale dataset for instance segmentation in aerial images"
    - "report on ug 2 challenge track 1 assessing algorithms to improve video object detection and classification from unconstrained mobility platforms"
    - "single image deraining a comprehensive benchmark analysis"
    - "dual directed capsule network for very low resolution image recognition"
    - "vision meets drones a challenge"
    - "the perception distortion tradeoff"
    - "crafting a toolchain for image restoration by deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "report on ug 2 challenge track 1 assessing algorithms to improve video object detection and classification from unconstrained mobility platforms"):
  with members:
    - "bridging the gap between computational photography and visual recognition"
    - "vision meets drones a challenge"
    - "the unreasonable effectiveness of texture transfer for single image super resolution"
    - "yolov3 an incremental improvement"
    - "task driven super resolution object detection in low resolution images"
    - "learning to see in the dark"
    - "enhancing the performance of convolutional neural networks on quality degraded datasets"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-3 (induced by source paper "edge aware deep image deblurring"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "wide activation for efficient and accurate image super resolution"
    - "motion deblurring in the wild"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "a neural approach to blind motion deblurring"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "from motion blur to motion flow a deep learning solution for removing heterogeneous motion blur"

Connected through hyperedge HE-4 (induced by source paper "integrating neural networks into the blind deblurring framework to compete with the end to end learning based methods"):
  with members:
    - "denoising prior driven deep neural network for image restoration"
    - "learning a discriminative prior for blind image deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "deep image matting"
    - "learning deep cnn denoiser prior for image restoration"
    - "detail revealing deep video super resolution"
    - "image restoration using autoencoding priors"
    - "video frame synthesis using deep voxel flow"

Connected through hyperedge HE-5 (induced by source paper "learning deep neural networks in blind deblurring framework"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "learning a discriminative prior for blind image deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "deep image matting"
    - "learning deep cnn denoiser prior for image restoration"
    - "detail revealing deep video super resolution"
    - "image restoration using autoencoding priors"
    - "stacked hourglass networks for human pose estimation"

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"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"
    - "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"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "learning deformable kernels for image and video denoising"):
  with members:
    - "learning to see in the dark"
    - "unprocessing images for learned raw denoising"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "deep burst denoising"
    - "video frame interpolation via adaptive convolution"
    - "deformable convolutional networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"

Connected through hyperedge HE-8 (induced by source paper "bringing alive blurred moments"):
  with members:
    - "learning to extract a video sequence from a single motion blurred image"
    - "scale recurrent network for deep image deblurring"
    - "residual dense network for image super resolution"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "enhanced deep residual networks for single image super resolution"
    - "video frame synthesis using deep voxel flow"
    - "examining the impact of blur on recognition by convolutional networks"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

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)