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: "gated gan adversarial gated networks for multi collection style transfer"
  Abstract: Style transfer describes the rendering of an image’s semantic content as different artistic styles. Recently, generative adversarial networks (GANs) have emerged as an effective approach in style transfer by adversarially training the generator to synthesize convincing counterfeits. However, traditional GAN suffers from the mode collapse issue, resulting in unstable training and making style transfer quality difficult to guarantee. In addition, the GAN generator is only compatible with one style, so a series of GANs must be trained to provide users with choices to transfer more than one kind of style. In this paper, we focus on tackling these challenges and limitations to improve style transfer. We propose adversarial gated networks (Gated-GAN) to transfer multiple styles in a single model. The generative networks have three modules: an encoder, a gated transformer, and a decoder. Different styles can be achieved by passing input images through different branches of the gated transformer. To stabilize training, the encoder and decoder are combined as an auto-encoder to reconstruct the input images. The discriminative networks are used to distinguish whether the input image is a stylized or genuine image. An auxiliary classifier is used to recognize the style categories of transferred images, thereby helping the generative networks generate images in multiple styles. In addition, Gated-GAN makes it possible to explore a new style by investigating styles learned from artists or genres. Our extensive experiments demonstrate the stability and effectiveness of the proposed model for multi-style transfer.

Connected through hyperedge HE-1 (induced by source paper "p 2 gan efficient style transfer using single style image"):
  with members:
    - "attention aware multi stroke style transfer"
    - "arbitrary style transfer with deep feature reshuffle"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "neural style transfer a review"
    - "visual attribute transfer through deep image analogy"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "stylebank an explicit representation for neural image style transfer"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-2 (induced by source paper "fake generated painting detection via frequency analysis"):
  with members:
    - "face x ray for more general face forgery detection"
    - "attention aware multi stroke style transfer"
    - "distinguishing computer generated graphics from natural images based on sensor pattern noise and deep learning"
    - "fake colorized image detection"
    - "do gans leave artificial fingerprints"
    - "detection of deep network generated images using disparities in color components"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "image processing using multi code gan prior"):
  with members:
    - "seeing what a gan cannot generate"
    - "semantic hierarchy emerges in the deep generative representations for scene synthesis"
    - "few shot unsupervised image to image translation"
    - "on the steerability of generative adversarial networks"
    - "interpreting the latent space of gans for semantic face editing"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "latent convolutional models"

Connected through hyperedge HE-4 (induced by source paper "cascade attention guided residue learning gan for cross modal translation"):
  with members:
    - "creating a multitrack classical music performance dataset for multimodal music analysis challenges insights and applications"
    - "mirrorgan learning text to image generation by redescription"
    - "self supervised audio visual co segmentation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "speech2face learning the face behind a voice"
    - "video to video synthesis"
    - "towards audio to scene image synthesis using generative adversarial network"
    - "mc gan multi conditional generative adversarial network for image synthesis"

Connected through hyperedge HE-5 (induced by source paper "evolutionary generative adversarial networks"):
  with members:
    - "deep semantic hashing with generative adversarial networks"
    - "turning high dimensional optimization into computationally expensive optimization"
    - "stabilizing gan training with multiple random projections"
    - "spectral normalization for generative adversarial networks"
    - "vital visual tracking via adversarial learning"
    - "dual discriminator generative adversarial nets"
    - "multi agent diverse generative adversarial networks"
    - "evolving deep 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)