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: "xgan unsupervised image to image translation for many to many mappings"
  Abstract: Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter. Here we tackle the more generic problem of semantic style transfer: given two unpaired collections of images, we aim to learn a mapping between the corpus-level style of each collection, while preserving semantic content shared across the two domains. We introduce XGAN ("Cross-GAN"), a dual adversarial autoencoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the domain-to-domain image translations in both directions. We exploit ideas from the domain adaptation literature and define a semantic consistency loss which encourages the model to preserve semantics in the learned embedding space. We report promising qualitative results for the task of face-to-cartoon translation. The cartoon dataset we collected for this purpose will also be released as a new benchmark for semantic style transfer.

Connected through hyperedge HE-1 (induced by source paper "reusing discriminators for encoding towards unsupervised image to image translation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "travelgan image to image translation by transformation vector learning"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "patch based image inpainting with generative adversarial networks"
    - "drit diverse image to image translation via disentangled representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "unpaired image to image translation using adversarial consistency loss"):
  with members:
    - "breaking the cycle colleagues are all you need"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "unsupervised attention guided image to image translation"
    - "drit diverse image to image translation via disentangled representations"
    - "combogan unrestrained scalability for image domain translation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-3 (induced by source paper "co generation with gans using ais based hmc"):
  with members:
    - "max sliced wasserstein distance and its use for gans"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "generative modeling using the sliced wasserstein distance"
    - "mcgan mean and covariance feature matching gan"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-4 (induced by source paper "unsupervised image to image translation with self attention networks"):
  with members:
    - "arbitrary style transfer with style attentional networks"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "drit diverse image to image translation via disentangled representations"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "non local neural networks"
    - "combogan unrestrained scalability for image domain translation"

Connected through hyperedge HE-5 (induced by source paper "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"):
  with members:
    - "group normalization"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "unsupervised attention guided image to image translation"
    - "drit diverse image to image translation via disentangled representations"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-6 (induced by source paper "landmark assisted cyclegan for cartoon face generation"):
  with members:
    - "cgans with projection discriminator"
    - "multimodal unsupervised image to image translation"
    - "improving shape deformation in unsupervised image to image translation"
    - "the contextual loss for image transformation with non aligned data"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "drit diverse image to image translation via disentangled representations"
    - "image to image translation for domain adaptation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-7 (induced by source paper "breaking the cycle colleagues are all you need"):
  with members:
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "drit diverse image to image translation via disentangled representations"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "gang of gans generative adversarial networks with maximum margin ranking"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-8 (induced by source paper "max sliced wasserstein distance and its use for gans"):
  with members:
    - "sliced wasserstein generative models"
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "multimodal unsupervised image to image translation"
    - "improving gans using optimal transport"
    - "generative modeling using the sliced wasserstein distance"
    - "which training methods for gans do actually converge"
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative 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)