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: "convolutional sketch inversion"
  Abstract: In this paper, we use deep neural networks for inverting face sketches to synthesize photorealistic face images. We first construct a semi-simulated dataset containing a very large number of computer-generated face sketches with different styles and corresponding face images by expanding existing unconstrained face data sets. We then train models achieving state-of-the-art results on both computer-generated sketches and hand-drawn sketches by leveraging recent advances in deep learning such as batch normalization, deep residual learning, perceptual losses and stochastic optimization in combination with our new dataset. We finally demonstrate potential applications of our models in fine arts and forensic arts. In contrast to existing patch-based approaches, our deep-neural-network-based approach can be used for synthesizing photorealistic face images by inverting face sketches in the wild.

Connected through hyperedge HE-1 (induced by source paper "attribute guided sketch generation"):
  with members:
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "instagan instance aware image to image translation"
    - "r btn cross domain face composite and synthesis from limited facial patches"
    - "deformable gans for pose based human image generation"
    - "image generation from sketch constraint using contextual gan"
    - "unsupervised creation of parameterized avatars"
    - "unsupervised diverse colorization via generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "lucss language based user customized colourization of scene sketches"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "sketchyscene richly annotated scene sketches"
    - "texturegan controlling deep image synthesis with texture patches"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "outline colorization through tandem adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "synthesizing images of humans in unseen poses"):
  with members:
    - "deep view morphing"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "inferring and executing programs for visual reasoning"
    - "pose guided person image generation"
    - "a generative model of people in clothing"
    - "multi view image generation from a single view"
    - "convolutional pose machines"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"

Connected through hyperedge HE-4 (induced by source paper "learning to sketch with shortcut cycle consistency"):
  with members:
    - "sketch pix2seq a model to generate sketches of multiple categories"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "cross modal subspace learning for fine grained sketch based image retrieval"
    - "fader networks manipulating images by sliding attributes"
    - "a neural representation of sketch drawings"
    - "dualgan unsupervised dual learning for image to image translation"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-5 (induced by source paper "unsupervised facial geometry learning for sketch to photo synthesis"):
  with members:
    - "image segmentation using sparse subset selection"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "image to image translation with conditional adversarial networks"
    - "colorful image colorization"

Connected through hyperedge HE-6 (induced by source paper "texturegan controlling deep image synthesis with texture patches"):
  with members:
    - "multi style generative network for real time transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "real time user guided image colorization with learned deep priors"
    - "learning texture manifolds with the periodic spatial gan"
    - "auto painter cartoon image generation from sketch by using conditional generative adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning representations for automatic colorization"

Connected through hyperedge HE-7 (induced by source paper "pixelnn example based image synthesis"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "photographic image synthesis with cascaded refinement networks"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning representations for automatic colorization"
    - "stacked generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "generative image modeling using style and structure adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "omniart multi task deep learning for artistic data analysis"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "detecting people in artwork with cnns"
    - "cross stitch networks for multi task learning"
    - "multi task zero shot action recognition with prioritised data augmentation"
    - "deep residual learning for image recognition"
    - "large scale classification of fine art paintings learning the right metric on the right feature"
    - "quantifying creativity in art 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)