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: "sc fegan face editing generative adversarial network with user s sketch and color"
  Abstract: We present a novel image editing system that generates images as the user provides free-form mask, sketch and color as an input. Our system consist of a end-to-end trainable convolutional network. Contrary to the existing methods, our system wholly utilizes free-form user input with color and shape. This allows the system to respond to the user's sketch and color input, using it as a guideline to generate an image. In our particular work, we trained network with additional style loss which made it possible to generate realistic results, despite large portions of the image being removed. Our proposed network architecture SC-FEGAN is well suited to generate high quality synthetic image using intuitive user inputs.

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"
    - "interactive sketch fill multiclass sketch to image translation"
    - "faceshop deep sketch based face image editing"
    - "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 "towards photo realistic virtual try on by adaptively generating leftrightarrow preserving image content"):
  with members:
    - "maskgan towards diverse and interactive facial image manipulation"
    - "fashion minimal edits for outfit improvement"
    - "foreground aware image inpainting"
    - "semantic image synthesis with spatially adaptive normalization"
    - "fashion editing with multi scale attention normalization"
    - "learning based animation of clothing for virtual try on"
    - "pose guided fashion image synthesis using deep generative model"
    - "toward characteristic preserving image based virtual try on network"

Connected through hyperedge HE-3 (induced by source paper "cali sketch stroke calibration and completion for high quality face image generation from poorly drawn sketches"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "csgan cyclic synthesized generative adversarial networks for image to image transformation"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "sketchygan towards diverse and realistic sketch to image synthesis"

Connected through hyperedge HE-4 (induced by source paper "high fidelity face manipulation with extreme pose and expression"):
  with members:
    - "pose agnostic cross spectral hallucination via disentangling independent factors"
    - "generating diverse high fidelity images with vq vae 2"
    - "semantic image synthesis with spatially adaptive normalization"
    - "dual variational generation for low shot heterogeneous face recognition"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "video to video synthesis"
    - "global and local consistent wavelet domain age synthesis"
    - "introvae introspective variational autoencoders for photographic image synthesis"

Connected through hyperedge HE-5 (induced by source paper "region wise generative adversarial imageinpainting for large missing areas"):
  with members:
    - "pluralistic image completion"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "image inpainting for irregular holes using partial convolutions"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "pi rec progressive image reconstruction network with edge and color domain"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "smit stochastic multi label image to image translation"
    - "probabilistic semantic inpainting with pixel constrained cnns"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "image2stylegan how to edit the embedded images"):
  with members:
    - "seeing what a gan cannot generate"
    - "detecting overfitting of deep generative networks via latent recovery"
    - "image2stylegan how to embed images into the stylegan latent space"
    - "few shot video to video synthesis"
    - "semantic image synthesis with spatially adaptive normalization"
    - "video to video synthesis"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "fashion editing with adversarial parsing learning"):
  with members:
    - "towards multi pose guided virtual try on network"
    - "foreground aware image inpainting"
    - "semantic image synthesis with spatially adaptive normalization"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "faceshop deep sketch based face image editing"
    - "free form image inpainting with gated convolution"
    - "semi parametric image inpainting"
    - "generative image inpainting with contextual attention"

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)