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: "deep painterly harmonization"
  Abstract: Copying an element from a photo and pasting it into a painting is a challenging task. Applying photo compositing techniques in this context yields subpar results that look like a collage --- and existing painterly stylization algorithms, which are global, perform poorly when applied locally. We address these issues with a dedicated algorithm that carefully determines the local statistics to be transferred. We ensure both spatial and inter-scale statistical consistency and demonstrate that both aspects are key to generating quality results. To cope with the diversity of abstraction levels and types of paintings, we introduce a technique to adjust the parameters of the transfer depending on the painting. We show that our algorithm produces significantly better results than photo compositing or global stylization techniques and that it enables creative painterly edits that would be otherwise difficult to achieve.

Connected through hyperedge HE-1 (induced by source paper "singan learning a generative model from a single natural image"):
  with members:
    - "surprising effectiveness of few image unsupervised feature learning"
    - "non stationary texture synthesis by adversarial expansion"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "ingan capturing and remapping the dna of a natural image"
    - "the perception distortion tradeoff"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"

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 "defective samples simulation through neural style transfer for automatic surface defect segment"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"

Connected through hyperedge HE-4 (induced by source paper "deep image blending"):
  with members:
    - "esrgan enhanced super resolution generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "deep photo style transfer"
    - "gp gan towards realistic high resolution image blending"
    - "universal style transfer via feature transforms"
    - "deep image harmonization"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "teaching dnns to design fast fashion"):
  with members:
    - "fashioning with networks neural style transfer to design clothes"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "generative creativity adversarial learning for bionic design"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "design design inspiration from generative networks"
    - "multimodal unsupervised image to image translation"
    - "multi agent diverse generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "semantic image synthesis via adversarial learning"
    - "pixelnn example based image synthesis"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-7 (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-8 (induced by source paper "twin gan unpaired cross domain image translation with weight sharing gans"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "on convergence and stability of gans"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "modulating early visual processing by language"
    - "progressive growing of gans for improved quality stability and variation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "towards the automatic anime characters creation with generative adversarial 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.GR(Graphics)