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: "multi view data generation without view supervision"
  Abstract: The development of high-dimensional generative models has recently gained a great surge of interest with the introduction of variational auto-encoders and generative adversarial neural networks. Different variants have been proposed where the underlying latent space is structured, for example, based on attributes describing the data to generate. We focus on a particular problem where one aims at generating samples corresponding to a number of objects under various views. We assume that the distribution of the data is driven by two independent latent factors: the content, which represents the intrinsic features of an object, and the view, which stands for the settings of a particular observation of that object. Therefore, we propose a generative model and a conditional variant built on such a disentangled latent space. This approach allows us to generate realistic samples corresponding to various objects in a high variety of views. Unlike many multi-view approaches, our model doesn't need any supervision on the views but only on the content. Compared to other conditional generation approaches that are mostly based on binary or categorical attributes, we make no such assumption about the factors of variations. Our model can be used on problems with a huge, potentially infinite, number of categories. We experiment it on four image datasets on which we demonstrate the effectiveness of the model and its ability to generalize.

Connected through hyperedge HE-1 (induced by source paper "adversarial disentanglement with grouped observations"):
  with members:
    - "disentangling content and style via unsupervised geometry distillation"
    - "disentangling style and content in anime illustrations"
    - "invariant representations without adversarial training"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "exploring disentangled feature representation beyond face identification"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning latent subspaces in variational autoencoders"
    - "recent advances in autoencoder based representation learning"

Connected through hyperedge HE-2 (induced by source paper "unsupervised object segmentation by redrawing"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "multi object representation learning with iterative variational inference"
    - "high fidelity image generation with fewer labels"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "weakly supervised instance segmentation using class peak response"
    - "vector image generation by learning parametric layer decomposition"
    - "learning to segment via cut and paste"
    - "seigan towards compositional image generation by simultaneously learning to segment enhance and inpaint"

Connected through hyperedge HE-3 (induced by source paper "inductive guided filter real time deep image matting with weakly annotated masks on mobile devices"):
  with members:
    - "boundary aware network for fast and high accuracy portrait segmentation"
    - "alphagan generative adversarial networks for natural image matting"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "spectral normalization for generative adversarial networks"
    - "semantic human matting"
    - "fast end to end trainable guided filter"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "robust ordinal vae employing noisy pairwise comparisons for disentanglement"):
  with members:
    - "weakly supervised disentanglement by pairwise similarities"
    - "dual swap disentangling"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "information constraints on auto encoding variational bayes"
    - "age progression regression by conditional adversarial autoencoder"
    - "multi level variational autoencoder learning disentangled representations from grouped observations"
    - "deep convolutional inverse graphics network"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised disentanglement by pairwise similarities"):
  with members:
    - "learning disentangled representations with reference based variational autoencoders"
    - "dual swap disentangling"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "gaussian process prior variational autoencoders"
    - "information constraints on auto encoding variational bayes"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "multi level variational autoencoder learning disentangled representations from grouped observations"

Connected through hyperedge HE-6 (induced by source paper "multiple attribute text style transfer"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "style transfer as unsupervised machine translation"
    - "disentangled representation learning for non parallel text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "shaped shared private encoder decoder for text style adaptation"
    - "fighting offensive language on social media with unsupervised text style transfer"
    - "language style transfer from sentences with arbitrary unknown styles"

Connected through hyperedge HE-7 (induced by source paper "rare event detection using disentangled representation learning"):
  with members:
    - "clustering and unsupervised anomaly detection with l2 normalized deep auto encoder representations"
    - "multi level variational autoencoder learning disentangled representations from grouped observations"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "learning deep structured network for weakly supervised change detection"
    - "domain separation networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "generating sentences from a continuous space"
    - "stereo matching by training a convolutional neural network to compare image patches"

Connected through hyperedge HE-8 (induced by source paper "multi content gan for few shot font style transfer"):
  with members:
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "chinese typography transfer"
    - "deep image prior"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "multi view image generation from a single view"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "visual attribute transfer through deep image analogy"

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)