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: "blade filter learning for general purpose computational photography"
  Abstract: The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters. We describe a generalization of RAISR, which we name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable edge-adaptive filtering framework that is general, simple, computationally efficient, and useful for a wide range of problems in computational photography. We show applications to operations which may appear in a camera pipeline including denoising, demosaicing, and stylization.

Connected through hyperedge HE-1 (induced by source paper "image stylization from predefined to personalized"):
  with members:
    - "proximal alternating direction network a globally converged deep unrolling framework"
    - "universal denoising networks a novel cnn architecture for image denoising"
    - "perceptual losses for real time style transfer and super resolution"
    - "controlling perceptual factors in neural style transfer"

Connected through hyperedge HE-2 (induced by source paper "deformable kernel networks for joint image filtering"):
  with members:
    - "pixel adaptive convolutional neural networks"
    - "learning guided convolutional network for depth completion"
    - "video frame interpolation via adaptive convolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "burst denoising with kernel prediction networks"
    - "joint image filtering with deep convolutional networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-3 (induced by source paper "basis prediction networks for effective burst denoising with large kernels"):
  with members:
    - "residual non local attention networks for image restoration"
    - "multi kernel prediction networks for denoising of burst images"
    - "handheld mobile photography in very low light"
    - "iterative residual cnns for burst photography applications"
    - "identifying recurring patterns with deep neural networks for natural image denoising"
    - "non local recurrent network for image restoration"
    - "learning to see in the dark"
    - "deep burst denoising"

Connected through hyperedge HE-4 (induced by source paper "pixel adaptive filtering units"):
  with members:
    - "pixel adaptive convolutional neural networks"
    - "iterative joint image demosaicking and denoising using a residual denoising network"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "neural nearest neighbors networks"
    - "segmentation aware convolutional networks using local attention masks"
    - "convolutional networks with adaptive inference graphs"
    - "enhanced deep residual networks for single image super resolution"
    - "burst denoising with kernel prediction networks"

Connected through hyperedge HE-5 (induced by source paper "simultaneous fidelity and regularization learning for image restoration"):
  with members:
    - "learning to deblur images with exemplars"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "universal denoising networks a novel cnn architecture for image denoising"
    - "deep joint rain detection and removal from a single image"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "cascades of regression tree fields for image restoration"

Connected through hyperedge HE-6 (induced by source paper "fast trainable multiscale denoising"):
  with members:
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "raisr rapid and accurate image super resolution"

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)