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: "faceshop deep sketch based face image editing"
  Abstract: We present a novel system for sketch-based face image editing, enabling users to edit images in an intuitive manner by sketching a few strokes on a region of interest. Our interface features tools to express a desired image manipulation by providing both geometry and color constraints as user-drawn strokes. As an alternative to the direct user input, our proposed system naturally supports a copy-paste mode, which allows users to edit a given image region by using parts of another exemplar image without the need of hand-drawn sketching at all. The proposed interface runs in real-time and facilitates an interactive and iterative workflow to quickly express the intended edits. Our system is based on a novel sketch domain and a convolutional neural network trained end-to-end to automatically learn to render image regions corresponding to the input strokes. To achieve high quality and semantically consistent results we train our neural network on two simultaneous tasks, namely image completion and image translation. To the best of our knowledge, we are the first to combine these two tasks in a unified framework for interactive image editing. Our results show that the proposed sketch domain, network architecture, and training procedure generalize well to real user input and enable high quality synthesis results without additional post-processing. Moreover, the proposed fully-convolutional model does not rely on any multi-scale or cascaded network architecture to synthesize high-resolution images and can be trained end-to-end to produce images of arbitrary size.

Connected through hyperedge HE-1 (induced by source paper "deep plastic surgery robust and controllable image editing with human drawn sketches"):
  with members:
    - "an unpaired sketch to photo translation model"
    - "controllable artistic text style transfer via shape matching gan"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "interactive sketch fill multiclass sketch to image translation"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "free form image inpainting with gated convolution"

Connected through hyperedge HE-2 (induced by source paper "interactive sketch fill multiclass sketch to image translation"):
  with members:
    - "im2pencil controllable pencil illustration from photographs"
    - "semantic image synthesis with spatially adaptive normalization"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "which training methods for gans do actually converge"
    - "synthesizing programs for images using reinforced adversarial learning"

Connected through hyperedge HE-3 (induced by source paper "mask guided portrait editing with conditional gans"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "arbitrary style transfer with deep feature reshuffle"
    - "semi parametric image synthesis"
    - "ganimation anatomically aware facial animation from a single image"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

Connected through hyperedge HE-4 (induced by source paper "fashion editing with adversarial parsing learning"):
  with members:
    - "towards multi pose guided virtual try on network"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "foreground aware image inpainting"
    - "semantic image synthesis with spatially adaptive normalization"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "free form image inpainting with gated convolution"
    - "semi parametric image inpainting"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-5 (induced by source paper "two phase hair image synthesis by self enhancing generative model"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "learning dual convolutional neural networks for low level vision"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "residual dense network for image super resolution"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "mganet a robust model for quality enhancement of compressed video"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-6 (induced by source paper "render4completion synthesizing multi view depth maps for 3d shape completion"):
  with members:
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "pcn point completion network"
    - "occupancy networks learning 3d reconstruction in function space"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "foldingnet interpretable unsupervised learning on 3d point clouds"

Connected through hyperedge HE-7 (induced by source paper "sc fegan face editing generative adversarial network with user s sketch and color"):
  with members:
    - "image inpainting for irregular holes using partial convolutions"
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "guided image inpainting replacing an image region by pulling content from another image"
    - "generative image inpainting with contextual attention"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"

Connected through hyperedge HE-8 (induced by source paper "smart deep copy paste"):
  with members:
    - "image inpainting using block wise procedural training with annealed adversarial counterpart"
    - "a style based generator architecture for generative adversarial networks"
    - "guided image inpainting replacing an image region by pulling content from another image"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"
    - "deep bilateral learning for real time image enhancement"
    - "real time user guided image colorization with learned deep priors"
    - "improved training of wasserstein gans"

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)