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: "exemplar guided unsupervised image to image translation with semantic consistency"
  Abstract: Image-to-image translation has recently received significant attention due to advances in deep learning. Most works focus on learning either a one-to-one mapping in an unsupervised way or a many-to-many mapping in a supervised way. However, a more practical setting is many-to-many mapping in an unsupervised way, which is harder due to the lack of supervision and the complex inner- and cross-domain variations. To alleviate these issues, we propose the Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network which conditions the translation process on an exemplar image in the target domain. We assume that an image comprises of a content component which is shared across domains, and a style component specific to each domain. Under the guidance of an exemplar from the target domain we apply Adaptive Instance Normalization to the shared content component, which allows us to transfer the style information of the target domain to the source domain. To avoid semantic inconsistencies during translation that naturally appear due to the large inner- and cross-domain variations, we introduce the concept of feature masks that provide coarse semantic guidance without requiring the use of any semantic labels. Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process.

Connected through hyperedge HE-1 (induced by source paper "object centric image generation from layouts"):
  with members:
    - "interactive image generation using scene graphs"
    - "scene graph generation with external knowledge and image reconstruction"
    - "object driven text to image synthesis via adversarial training"
    - "image synthesis from reconfigurable layout and style"
    - "pastegan a semi parametric method to generate image from scene graph"
    - "locality and compositionality in zero shot learning"
    - "semantic image synthesis with spatially adaptive normalization"
    - "semantics disentangling for text to image generation"

Connected through hyperedge HE-2 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "joint discriminative and generative learning for person re identification"
    - "consistency regularization for generative adversarial networks"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "semantic image synthesis with spatially adaptive normalization"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-3 (induced by source paper "emerging disentanglement in auto encoder based unsupervised image content transfer"):
  with members:
    - "a two step disentanglement method"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "dida disentangled synthesis for domain adaptation"
    - "image to image translation for cross domain disentanglement"
    - "drit diverse image to image translation via disentangled representations"
    - "infovae information maximizing variational autoencoders"
    - "emergence of invariance and disentanglement in deep representations"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "example guided style consistent image synthesis from semantic labeling"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "multimodal unsupervised image to image translation"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "towards open set identity preserving face synthesis"
    - "arbitrary style transfer with deep feature reshuffle"
    - "semi parametric image synthesis"
    - "dual attention network for scene segmentation"
    - "densepose dense human pose estimation in the wild"

Connected through hyperedge HE-5 (induced by source paper "example guided scene image synthesis using masked spatial channel attention and patch based self supervision"):
  with members:
    - "example guided style consistent image synthesis from semantic labeling"
    - "semantic image synthesis with spatially adaptive normalization"
    - "photorealistic style transfer via wavelet transforms"
    - "video to video synthesis"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "multimodal unsupervised image to image translation"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "stargan v2 diverse image synthesis for multiple domains"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "coloring with limited data few shot colorization via memory augmented networks"
    - "tag2pix line art colorization using text tag with secat and changing loss"
    - "multi mapping image to image translation via learning disentanglement"
    - "large scale adversarial representation learning"
    - "few shot unsupervised image to image translation"
    - "sdit scalable and diverse cross domain image translation"
    - "semantic image synthesis with spatially adaptive normalization"

Connected through hyperedge HE-7 (induced by source paper "gmm unit unsupervised multi domain and multi modal image to image translation via attribute gaussian mixture modelling"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "transgaga geometry aware unsupervised image to image translation"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-8 (induced by source paper "a novel bilevel paradigm for image to image translation"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "few shot segmentation propagation with guided networks"
    - "every smile is unique landmark guided diverse smile generation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "taskonomy disentangling task transfer learning"

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)