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: "generating handwritten chinese characters using cyclegan"
  Abstract: Handwriting of Chinese has long been an important skill in East Asia. However, automatic generation of handwritten Chinese characters poses a great challenge due to the large number of characters. Various machine learning techniques have been used to recognize Chinese characters, but few works have studied the handwritten Chinese character generation problem, especially with unpaired training data. In this work, we formulate the Chinese handwritten character generation as a problem that learns a mapping from an existing printed font to a personalized handwritten style. We further propose DenseNet CycleGAN to generate Chinese handwritten characters. Our method is applied not only to commonly used Chinese characters but also to calligraphy work with aesthetic values. Furthermore, we propose content accuracy and style discrepancy as the evaluation metrics to assess the quality of the handwritten characters generated. We then use our proposed metrics to evaluate the generated characters from CASIA dataset as well as our newly introduced Lanting calligraphy dataset.

Connected through hyperedge HE-1 (induced by source paper "ganwriting content conditioned generation of styled handwritten word images"):
  with members:
    - "adversarial generation of handwritten text images conditioned on sequences"
    - "few shot unsupervised image to image translation"
    - "unsupervised writer adaptation for synthetic to real handwritten word recognition"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-2 (induced by source paper "fontgan a unified generative framework for chinese character stylization and de stylization"):
  with members:
    - "automatic generation of typographic font from small font subset"
    - "global and local consistent wavelet domain age synthesis"
    - "drit diverse image to image translation via disentangled representations"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "toward multimodal image to image translation"
    - "separating style and content for generalized style transfer"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-3 (induced by source paper "adversarial generation of handwritten text images conditioned on sequences"):
  with members:
    - "geometry score a method for comparing generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "modulating early visual processing by language"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "generative adversarial text to image synthesis"
    - "least squares generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "modular generative adversarial networks"):
  with members:
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "a hybrid model for identity obfuscation by face replacement"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "inferring and executing programs for visual reasoning"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "visual reference resolution using attention memory for visual dialog"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "coconditional autoencoding adversarial networks for chinese font feature learning"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "unsupervised typography transfer"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "chinese typography transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "invertible conditional gans for image editing"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "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)