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 guided filter"
  Abstract: The guided filter is a technique for edge-aware image filtering. Because of its nice visual quality, fast speed, and ease of implementation, the guided filter has witnessed various applications in real products, such as image editing apps in phones and stereo reconstruction, and has been included in official MATLAB and OpenCV. In this note, we remind that the guided filter can be simply sped up from O(N) time to O(N/s^2) time for a subsampling ratio s. In a variety of applications, this leads to a speedup of >10x with almost no visible degradation. We hope this acceleration will improve performance of current applications and further popularize this filter. Code is released.

Connected through hyperedge HE-1 (induced by source paper "famed net a fast and accurate multi scale end to end dehazing network"):
  with members:
    - "improved techniques for learning to dehaze and beyond a collective study"
    - "fully point wise convolutional neural network for modeling statistical regularities in natural images"
    - "semantic foggy scene understanding with synthetic data"
    - "lapran a scalable laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction"
    - "gated fusion network for single image dehazing"
    - "deep retinex decomposition for low light enhancement"
    - "densely connected pyramid dehazing network"
    - "end to end united video dehazing and detection"

Connected through hyperedge HE-2 (induced by source paper "adaptive composition gan towards realistic image synthesis"):
  with members:
    - "scene text synthesis for efficient and effective deep network training"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "compositional gan learning conditional image composition"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "inductive guided filter real time deep image matting with weakly annotated masks on mobile devices"):
  with members:
    - "boundary aware network for fast and high accuracy portrait segmentation"
    - "alphagan generative adversarial networks for natural image matting"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "spectral normalization for generative adversarial networks"
    - "semantic human matting"
    - "fast end to end trainable guided filter"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "a general decoupled learning framework for parameterized image operators"):
  with members:
    - "clearing the skies a deep network architecture for single image rain removal"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "deep bilateral learning for real time image enhancement"
    - "learned optimizers that scale and generalize"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-5 (induced by source paper "rain o er me synthesizing real rain to derain with data distillation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deep mutual learning"
    - "deep layer aggregation"
    - "data distillation towards omni supervised learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "deep joint rain detection and removal from a single image"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "spatial fusion gan for image synthesis"):
  with members:
    - "scene text synthesis for efficient and effective deep network training"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "compositional gan learning conditional image composition"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "esir end to end scene text recognition via iterative image rectification"
    - "compositional gan learning image conditional binary composition"
    - "fast end to end trainable guided filter"

Connected through hyperedge HE-7 (induced by source paper "hardware efficient guided image filtering for multi label problem"):
  with members:
    - "speeding up the bilateral filter a joint acceleration way"

Connected through hyperedge HE-8 (induced by source paper "fast image processing with fully convolutional networks"):
  with members:
    - "photographic image synthesis with cascaded refinement networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "unrolled generative adversarial networks"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "image to image translation with conditional adversarial networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

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)