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: "makeup like a superstar deep localized makeup transfer network"
  Abstract: In this paper, we propose a novel Deep Localized Makeup Transfer Network to automatically recommend the most suitable makeup for a female and synthesis the makeup on her face. Given a before-makeup face, her most suitable makeup is determined automatically. Then, both the beforemakeup and the reference faces are fed into the proposed Deep Transfer Network to generate the after-makeup face. Our end-to-end makeup transfer network have several nice properties including: (1) with complete functions: including foundation, lip gloss, and eye shadow transfer; (2) cosmetic specific: different cosmetics are transferred in different manners; (3) localized: different cosmetics are applied on different facial regions; (4) producing naturally looking results without obvious artifacts; (5) controllable makeup lightness: various results from light makeup to heavy makeup can be generated. Qualitative and quantitative experiments show that our network performs much better than the methods of [Guo and Sim, 2009] and two variants of NerualStyle [Gatys et al., 2015a].

Connected through hyperedge HE-1 (induced by source paper "controllable descendant face synthesis"):
  with members:
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "kinshipgan synthesizing of kinship faces from family photos by regularizing a deep face network"
    - "unsupervised holistic image generation from key local patches"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "age progression regression by conditional adversarial autoencoder"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-2 (induced by source paper "content and colour distillation for learning image translations with the spatial profile loss"):
  with members:
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "improving gan training via binarized representation entropy bre regularization"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the contextual loss for image transformation with non aligned data"
    - "which training methods for gans do actually converge"
    - "perceptual adversarial networks for image to image transformation"

Connected through hyperedge HE-3 (induced by source paper "disentangled makeup transfer with generative adversarial network"):
  with members:
    - "show attend and translate unsupervised image translation with self regularization and attention"
    - "maskgan towards diverse and interactive facial image manipulation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "drit diverse image to image translation via disentangled representations"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "psgan pose robust spatial aware gan for customizable makeup transfer"):
  with members:
    - "non local neural networks"
    - "attention is all you need"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "deep photo style transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "visual attribute transfer through deep image analogy"
    - "perceptual losses for real time style transfer and super resolution"
    - "unsupervised cross domain image generation"

Connected through hyperedge HE-5 (induced by source paper "psgan pose and expression robust spatial aware gan for customizable makeup transfer"):
  with members:
    - "ladn local adversarial disentangling network for facial makeup and de makeup"
    - "non local neural networks"
    - "attention is all you need"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "deep photo style transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "visual attribute transfer through deep image analogy"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-6 (induced by source paper "ladn local adversarial disentangling network for facial makeup and de makeup"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "dida disentangled synthesis for domain adaptation"
    - "drit diverse image to image translation via disentangled representations"
    - "generative face completion"
    - "smart mirror intelligent makeup recommendation and synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "visual attribute transfer through deep image analogy"

Connected through hyperedge HE-7 (induced by source paper "facial makeup transfer combining illumination transfer"):
  with members:
    - "brain intelligence go beyond artificial intelligence"
    - "single reference image based scene relighting via material guided filtering"

Connected through hyperedge HE-8 (induced by source paper "digital makeup from internet images"):
  with members:
    - "a neural algorithm of artistic style"
    - "deep convolutional inverse graphics network"
    - "matching cnn meets knn quasi parametric human parsing"
    - "deep human parsing with active template regression"
    - "understanding deep image representations by inverting them"
    - "learning to generate chairs with convolutional neural networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

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)