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: "xunit learning a spatial activation function for efficient image restoration"
  Abstract: In recent years, deep neural networks (DNNs) achieved unprecedented performance in many low-level vision tasks. However, state-of-the-art results are typically achieved by very deep networks, which can reach tens of layers with tens of millions of parameters. To make DNNs implementable on platforms with limited resources, it is necessary to weaken the tradeoff between performance and efficiency. In this paper, we propose a new activation unit, which is particularly suitable for image restoration problems. In contrast to the widespread per-pixel activation units, like ReLUs and sigmoids, our unit implements a learnable nonlinear function with spatial connections. This enables the net to capture much more complex features, thus requiring a significantly smaller number of layers in order to reach the same performance. We illustrate the effectiveness of our units through experiments with state-of-the-art nets for denoising, de-raining, and super resolution, which are already considered to be very small. With our approach, we are able to further reduce these models by nearly 50% without incurring any degradation in performance.

Connected through hyperedge HE-1 (induced by source paper "learning delicate local representations for multi person pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "res2net a new multi scale backbone architecture"
    - "multi person pose estimation with enhanced channel wise and spatial information"
    - "omni scale feature learning for person re identification"
    - "deep high resolution representation learning for human pose estimation"
    - "quantized densely connected u nets for efficient landmark localization"
    - "learning a discriminative feature network for semantic segmentation"
    - "multi scale structure aware network for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "restoring images with unknown degradation factors by recurrent use of a multi branch network"):
  with members:
    - "dual residual networks leveraging the potential of paired operations for image restoration"
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "gated fusion network for single image dehazing"
    - "image super resolution using very deep residual channel attention networks"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "non locally enhanced encoder decoder network for single image de raining"

Connected through hyperedge HE-3 (induced by source paper "explorable super resolution"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "blind super resolution kernel estimation using an internal gan"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "the relativistic discriminator a key element missing from standard gan"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "a fully progressive approach to single image super resolution"
    - "a style based generator architecture for generative adversarial networks"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-4 (induced by source paper "few shot meta denoising"):
  with members:
    - "adversarial signal denoising with encoder decoder networks"
    - "sharpness aware low dose ct denoising using conditional generative adversarial network"
    - "unprocessing images for learned raw denoising"
    - "prototypical networks for few shot learning"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a wavenet for speech denoising"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-5 (induced by source paper "blind visual motif removal from a single image"):
  with members:
    - "generative single image reflection separation"
    - "single image reflection separation with perceptual losses"
    - "generative image inpainting with contextual attention"
    - "image restoration by iterative denoising and backward projections"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "deep blind image inpainting"
    - "image de raining using a conditional generative adversarial network"
    - "a generic deep architecture for single image reflection removal and image smoothing"

Connected through hyperedge HE-6 (induced by source paper "dense xunit networks"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a fully progressive approach to single image super resolution"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "enhanced deep residual networks for single image super resolution"
    - "learning deep cnn denoiser prior for image restoration"

Connected through hyperedge HE-7 (induced by source paper "decouple learning for parameterized image operators"):
  with members:
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "stereoscopic neural style transfer"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "density aware single image de raining using a multi stream dense network"
    - "deep exemplar based colorization"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "global versus localized generative adversarial nets"

Connected through hyperedge HE-8 (induced by source paper "pirm challenge on perceptual image enhancement on smartphones report"):
  with members:
    - "pirm2018 challenge on spectral image super resolution dataset and study"
    - "convolutional neural networks with alternately updated clique"
    - "the relativistic discriminator a key element missing from standard gan"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "ai benchmark running deep neural networks on android smartphones"
    - "fast perceptual image enhancement"
    - "deep back projection networks for super resolution"
    - "dslr quality photos on mobile devices with deep convolutional networks"

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)