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: "logo synthesis and manipulation with clustered generative adversarial networks"
  Abstract: Designing a logo for a new brand is a lengthy and tedious back-and-forth process between a designer and a client. In this paper we explore to what extent machine learning can solve the creative task of the designer. For this, we build a dataset -- LLD -- of 600k+ logos crawled from the world wide web. Training Generative Adversarial Networks (GANs) for logo synthesis on such multi-modal data is not straightforward and results in mode collapse for some state-of-the-art methods. We propose the use of synthetic labels obtained through clustering to disentangle and stabilize GAN training. We are able to generate a high diversity of plausible logos and we demonstrate latent space exploration techniques to ease the logo design task in an interactive manner. Moreover, we validate the proposed clustered GAN training on CIFAR 10, achieving state-of-the-art Inception scores when using synthetic labels obtained via clustering the features of an ImageNet classifier. GANs can cope with multi-modal data by means of synthetic labels achieved through clustering, and our results show the creative potential of such techniques for logo synthesis and manipulation. Our dataset and models will be made publicly available at this https URL

Connected through hyperedge HE-1 (induced by source paper "loganv2 conditional style based logo generation with generative adversarial networks"):
  with members:
    - "logan generating logos with a generative adversarial neural network conditioned on color"
    - "a style based generator architecture for generative adversarial networks"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "megapixel size image creation using generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "trick or treat thematic reinforcement for artistic typography"):
  with members:
    - "contained neural style transfer for decorated logo generation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi content gan for few shot font style transfer"
    - "a neural representation of sketch drawings"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "image to image translation with conditional adversarial networks"
    - "character sequence models for colorfulwords"
    - "towards universal paraphrastic sentence embeddings"

Connected through hyperedge HE-3 (induced by source paper "logan generating logos with a generative adversarial neural network conditioned on color"):
  with members:
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "on convergence and stability of gans"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "towards the automatic anime characters creation with generative adversarial networks"
    - "perceptual generative adversarial networks for small object detection"
    - "improved training of wasserstein gans"
    - "gp gan towards realistic high resolution image blending"

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)