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: "gated fusion network for joint image deblurring and super resolution"
  Abstract: Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution. If the input image contains degraded pixels, the artifacts caused by the degradation could be amplified by super-resolution methods. Image blur is a common degradation source. Images captured by moving or still cameras are inevitably affected by motion blur due to relative movements between sensors and objects. In this work, we focus on the super-resolution task with the presence of motion blur. We propose a deep gated fusion convolution neural network to generate a clear high-resolution frame from a single natural image with severe blur. By decomposing the feature extraction step into two task-independent streams, the dual-branch design can facilitate the training process by avoiding learning the mixed degradation all-in-one and thus enhance the final high-resolution prediction results. Extensive experiments demonstrate that our method generates sharper super-resolved images from low-resolution inputs with high computational efficiency.

Connected through hyperedge HE-1 (induced by source paper "handling noise in image deblurring via joint learning"):
  with members:
    - "efficient blind deblurring under high noise levels"
    - "scale recurrent network for deep image deblurring"
    - "learning to deblur images with exemplars"
    - "toward convolutional blind denoising of real photographs"
    - "deep semantic face deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"

Connected through hyperedge HE-2 (induced by source paper "deblurgan v2 deblurring orders of magnitude faster and better"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "single image deraining a comprehensive benchmark analysis"
    - "enlightengan deep light enhancement without paired supervision"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the relativistic discriminator a key element missing from standard gan"
    - "scale recurrent network for deep image deblurring"
    - "yolov3 an incremental improvement"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"

Connected through hyperedge HE-3 (induced by source paper "deep plug and play super resolution for arbitrary blur kernels"):
  with members:
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "regularization by denoising clarifications and new interpretations"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "neural nearest neighbors networks"
    - "deep cnn denoiser and multi layer neighbor component embedding for face hallucination"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-4 (induced by source paper "event driven video frame synthesis"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "learning to extract a video sequence from a single motion blurred image"
    - "continuous time intensity estimation using event cameras"
    - "bringing a blurry frame alive at high frame rate with an event camera"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "compressive holographic video"

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)