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: "double dip unsupervised image decomposition via coupled deep image priors"
  Abstract: Many seemingly unrelated computer vision tasks can be viewed as a special case of image decomposition into separate layers. For example, image segmentation (separation into foreground and background layers); transparent layer separation (into reflection and transmission layers); Image dehazing (separation into a clear image and a haze map), and more. In this paper we propose a unified framework for unsupervised layer decomposition of a single image, based on coupled "Deep-image-Prior" (DIP) networks. It was shown [Ulyanov et al] that the structure of a single DIP generator network is sufficient to capture the low-level statistics of a single image. We show that coupling multiple such DIPs provides a powerful tool for decomposing images into their basic components, for a wide variety of applications. This capability stems from the fact that the internal statistics of a mixture of layers is more complex than the statistics of each of its individual components. We show the power of this approach for Image-Dehazing, Fg/Bg Segmentation, Watermark-Removal, Transparency Separation in images and video, and more. These capabilities are achieved in a totally unsupervised way, with no training examples other than the input image/video itself.

Connected through hyperedge HE-1 (induced by source paper "semantic pyramid for image generation"):
  with members:
    - "seeing what a gan cannot generate"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "on the steerability of generative adversarial networks"
    - "image resizing by reconstruction from deep features"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-2 (induced by source paper "neural architecture search for deep image prior"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "autogan neural architecture search for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "progressive neural architecture search"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "large scale evolution of image classifiers"
    - "efficient architecture search by network transformation"

Connected through hyperedge HE-3 (induced by source paper "deep depth prior for multi view stereo"):
  with members:
    - "a bayesian perspective on the deep image prior"
    - "learned collaborative stereo refinement"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "deepmvs learning multi view stereopsis"
    - "continuous 3d label stereo matching using local expansion moves"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "pyramid stereo matching network"
    - "deep geometric prior for surface reconstruction"

Connected through hyperedge HE-4 (induced by source paper "controllable attention for structured layered video decomposition"):
  with members:
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "the conversation deep audio visual speech enhancement"
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "a short note about kinetics 600"
    - "single image reflection removal using deep encoder decoder network"
    - "single image reflection separation with perceptual losses"
    - "learning to separate object sounds by watching unlabeled video"

Connected through hyperedge HE-5 (induced by source paper "deep audio prior"):
  with members:
    - "photometric mesh optimization for video aligned 3d object reconstruction"
    - "audio denoising with deep network priors"
    - "self supervised generation of spatial audio for 360 video"
    - "single image reflection separation with perceptual losses"
    - "does k matter k nn hubness analysis for kernel additive modelling vocal separation"
    - "improving dnn based music source separation using phase features"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "deep attractor network for single microphone speaker separation"

Connected through hyperedge HE-6 (induced by source paper "an internal learning approach to video inpainting"):
  with members:
    - "deep flow guided video inpainting"
    - "deep video inpainting"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "everybody dance now"
    - "generative image inpainting with contextual attention"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "characterizing and improving stability in neural style transfer"

Connected through hyperedge HE-7 (induced by source paper "blind image deconvolution using pretrained generative priors"):
  with members:
    - "deep ptych subsampled fourier ptychography using generative priors"
    - "learning a discriminative prior for blind image deblurring"
    - "latent convolutional models"
    - "phase retrieval under a generative prior"
    - "robust compressive phase retrieval via deep generative priors"
    - "progressive growing of gans for improved quality stability and variation"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "global guarantees for enforcing deep generative priors by empirical risk"

Connected through hyperedge HE-8 (induced by source paper "computational mirrors blind inverse light transport by deep matrix factorization"):
  with members:
    - "a bayesian perspective on the deep image prior"
    - "neural blind deconvolution using deep priors"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "blind image deconvolution using deep generative priors"
    - "deep image prior"
    - "optically lightweight tracking of objects around a corner"
    - "a deep matrix factorization method for learning attribute representations"
    - "adam a method for stochastic optimization"

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)