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: "learning typographic style"
  Abstract: Typography is a ubiquitous art form that affects our understanding, perception, and trust in what we read. Thousands of different font-faces have been created with enormous variations in the characters. In this paper, we learn the style of a font by analyzing a small subset of only four letters. From these four letters, we learn two tasks. The first is a discrimination task: given the four letters and a new candidate letter, does the new letter belong to the same font? Second, given the four basis letters, can we generate all of the other letters with the same characteristics as those in the basis set? We use deep neural networks to address both tasks, quantitatively and qualitatively measure the results in a variety of novel manners, and present a thorough investigation of the weaknesses and strengths of the approach.

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 "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-3 (induced by source paper "multi content gan for few shot font style transfer"):
  with members:
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "chinese typography transfer"
    - "multi view data generation without view supervision"
    - "deep image prior"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "multi view image generation from a single view"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "learning to write stylized chinese characters by reading a handful of examples"):
  with members:
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "learning hierarchical features from generative models"
    - "multi level variational autoencoder learning disentangled representations from grouped observations"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "max margin deep generative models for semi supervised learning"
    - "densely connected convolutional networks"
    - "adversarial feature learning"

Connected through hyperedge HE-5 (induced by source paper "generative adversarial networks with inverse transformation unit"):
  with members:
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "unsupervised cross domain image generation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "energy based generative adversarial network"
    - "generating images with recurrent adversarial networks"
    - "from a to z supervised transfer of style and content using deep neural network generators"

Connected through hyperedge HE-6 (induced by source paper "chinese typography transfer"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "fully convolutional networks for semantic segmentation"

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)