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: "from a to z supervised transfer of style and content using deep neural network generators"
  Abstract: We propose a new neural network architecture for solving single-image analogies - the generation of an entire set of stylistically similar images from just a single input image. Solving this problem requires separating image style from content. Our network is a modified variational autoencoder (VAE) that supports supervised training of single-image analogies and in-network evaluation of outputs with a structured similarity objective that captures pixel covariances. On the challenging task of generating a 62-letter font from a single example letter we produce images with 22.4% lower dissimilarity to the ground truth than state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "artistic glyph image synthesis via one stage few shot learning"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "figr few shot image generation with reptile"
    - "few shot unsupervised image to image translation"
    - "arbitrary style transfer with deep feature reshuffle"
    - "tet gan text effects transfer via stylization and destylization"
    - "the contextual loss for image transformation with non aligned data"
    - "image to image translation for cross domain disentanglement"
    - "style and content disentanglement in generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "deep parametric shape predictions using distance fields"):
  with members:
    - "deepspline data driven reconstruction of parametric curves and surfaces"
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"
    - "vectorization of line drawings via polyvector fields"
    - "im2struct recovering 3d shape structure from a single rgb image"

Connected through hyperedge HE-3 (induced by source paper "handwritten chinese font generation with collaborative stroke refinement"):
  with members:
    - "learning discriminative video representations using adversarial perturbations"
    - "a style aware content loss for real time hd style transfer"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "learning to write stylized chinese characters by reading a handful of examples"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "multi content gan for few shot font style transfer"

Connected through hyperedge HE-4 (induced by source paper "stefann scene text editor using font adaptive neural network"):
  with members:
    - "learning to read by spelling towards unsupervised text recognition"
    - "emerging from water underwater image color correction based on weakly supervised color transfer"
    - "edit probability for scene text recognition"
    - "multi oriented scene text detection via corner localization and region segmentation"
    - "chinese typography transfer"
    - "east an efficient and accurate scene text detector"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "visual attribute transfer through deep image analogy"

Connected through hyperedge HE-5 (induced by source paper "learning icons appearance similarity"):
  with members:
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning style similarity for searching infographics"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "a unified framework for generalizable style transfer style and content separation"):
  with members:
    - "demystifying neural style transfer"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "multi style generative network for real time transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi content gan for few shot font style transfer"

Connected through hyperedge HE-7 (induced by source paper "typeface completion with generative adversarial networks"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "multimodal unsupervised image to image translation"
    - "word level font to font image translation using convolutional recurrent generative adversarial networks"
    - "chinese typography transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi content gan for few shot font style transfer"

Connected through hyperedge HE-8 (induced by source paper "generalizing across domains via cross gradient training"):
  with members:
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "adversarial discriminative domain adaptation"
    - "unified deep supervised domain adaptation and generalization"
    - "zero shot deep domain adaptation"
    - "simultaneous deep transfer across domains and tasks"
    - "deep residual learning for image recognition"
    - "domain generalization for object recognition with multi task autoencoders"
    - "intriguing properties of neural 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)