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 to write stylized chinese characters by reading a handful of examples"
  Abstract: Automatically writing stylized Chinese characters is an attractive yet challenging task due to its wide applicabilities. In this paper, we propose a novel framework named Style-Aware Variational Auto-Encoder (SA-VAE) to flexibly generate Chinese characters. Specifically, we propose to capture the different characteristics of a Chinese character by disentangling the latent features into content-related and style-related components. Considering of the complex shapes and structures, we incorporate the structure information as prior knowledge into our framework to guide the generation. Our framework shows a powerful one-shot/low-shot generalization ability by inferring the style component given a character with unseen style. To the best of our knowledge, this is the first attempt to learn to write new-style Chinese characters by observing only one or a few examples. Extensive experiments demonstrate its effectiveness in generating different stylized Chinese characters by fusing the feature vectors corresponding to different contents and styles, which is of significant importance in real-world applications.

Connected through hyperedge HE-1 (induced by source paper "editing text in the wild"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "stefann scene text editor using font adaptive neural network"
    - "synthesizing images of humans in unseen poses"
    - "spectral normalization for generative adversarial networks"
    - "tet gan text effects transfer via stylization and destylization"
    - "scene text detection and recognition the deep learning era"
    - "ensnet ensconce text in the wild"

Connected through hyperedge HE-2 (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"
    - "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"
    - "separating style and content for generalized style transfer"

Connected through hyperedge HE-3 (induced by source paper "multi reference tacotron by intercross training for style disentangling transfer and control in speech synthesis"):
  with members:
    - "learning latent representations for style control and transfer in end to end speech synthesis"
    - "close to human quality tts with transformer"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "neural speech synthesis with transformer network"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "deep voice 3 2000 speaker neural text to speech"

Connected through hyperedge HE-4 (induced by source paper "tet gan text effects transfer via stylization and destylization"):
  with members:
    - "image to image translation for domain adaptation"
    - "demystifying neural style transfer"
    - "progressive growing of gans for improved quality stability and variation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "multi content gan for few shot font style transfer"

Connected through hyperedge HE-5 (induced by source paper "generating handwritten chinese characters using cyclegan"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "neural style transfer a review"
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "fast patch based style transfer of arbitrary style"
    - "improved techniques for training gans"
    - "densely connected convolutional networks"
    - "image to image translation with conditional adversarial 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)