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: "fast image processing with fully convolutional networks"
  Abstract: We present an approach to accelerating a wide variety of image processing operators. Our approach uses a fully-convolutional network that is trained on input-output pairs that demonstrate the operator's action. After training, the original operator need not be run at all. The trained network operates at full resolution and runs in constant time. We investigate the effect of network architecture on approximation accuracy, runtime, and memory footprint, and identify a specific architecture that balances these considerations. We evaluate the presented approach on ten advanced image processing operators, including multiple variational models, multiscale tone and detail manipulation, photographic style transfer, nonlocal dehazing, and nonphotorealistic stylization. All operators are approximated by the same model. Experiments demonstrate that the presented approach is significantly more accurate than prior approximation schemes. It increases approximation accuracy as measured by PSNR across the evaluated operators by 8.5 dB on the MIT-Adobe dataset (from 27.5 to 36 dB) and reduces DSSIM by a multiplicative factor of 3 compared to the most accurate prior approximation scheme, while being the fastest. We show that our models generalize across datasets and across resolutions, and investigate a number of extensions of the presented approach. The results are shown in the supplementary video at this https URL

Connected through hyperedge HE-1 (induced by source paper "getting to 99 accuracy in interactive segmentation"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "weight standardization"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "group normalization"
    - "interactive video object segmentation in the wild"
    - "iteratively trained interactive segmentation"
    - "fast end to end trainable guided filter"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep image compression using decoder side information"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "conditional probability models for deep image compression"
    - "video compression through image interpolation"
    - "learning binary residual representations for domain specific video streaming"
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimized image compression"
    - "variable rate image compression with recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "ecnn a block based and highly parallel cnn accelerator for edge inference"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "morph flexible acceleration for 3d cnn based video understanding"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "scale sim systolic cnn accelerator simulator"
    - "eva 2 exploiting temporal redundancy in live computer vision"
    - "in datacenter performance analysis of a tensor processing unit"
    - "dslr quality photos on mobile devices with deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "improving the harmony of the composite image by spatial separated attention module"):
  with members:
    - "inserting videos into videos"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "selective kernel networks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "temporally coherent video harmonization using adversarial networks"
    - "attention gan for object transfiguration in wild images"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-5 (induced by source paper "learning digital camera pipeline for extreme low light imaging"):
  with members:
    - "image super resolution using very deep residual channel attention networks"
    - "toward convolutional blind denoising of real photographs"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "deep exemplar based colorization"
    - "learning to see in the dark"
    - "pieapp perceptual image error assessment through pairwise preference"
    - "learned perceptual image enhancement"

Connected through hyperedge HE-6 (induced by source paper "im2pencil controllable pencil illustration from photographs"):
  with members:
    - "a style aware content loss for real time hd style transfer"
    - "multimodal unsupervised image to image translation"
    - "line drawings from 3d models"
    - "drit diverse image to image translation via disentangled representations"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "towards ghost free shadow removal via dual hierarchical aggregation network and shadow matting gan"):
  with members:
    - "mask shadowgan learning to remove shadows from unpaired data"
    - "shadow removal via shadow image decomposition"
    - "argan attentive recurrent generative adversarial network for shadow detection and removal"
    - "free form image inpainting with gated convolution"
    - "single image reflection separation with perceptual losses"
    - "conditional gans for multi illuminant color constancy revolution or yet another approach"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "direction aware spatial context features for shadow detection and removal"

Connected through hyperedge HE-8 (induced by source paper "ernet family hardware oriented cnn models for computational imaging using block based inference"):
  with members:
    - "ecnn a block based and highly parallel cnn accelerator for edge inference"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "wide activation for efficient and accurate image super resolution"
    - "object detection with deep learning a review"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "enhanced deep residual networks for single image super resolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

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)