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: "learning diverse image colorization"
  Abstract: Colorization is an ambiguous problem, with multiple viable colorizations for a single grey-level image. However, previous methods only produce the single most probable colorization. Our goal is to model the diversity intrinsic to the problem of colorization and produce multiple colorizations that display long-scale spatial co-ordination. We learn a low dimensional embedding of color fields using a variational autoencoder (VAE). We construct loss terms for the VAE decoder that avoid blurry outputs and take into account the uneven distribution of pixel colors. Finally, we build a conditional model for the multi-modal distribution between grey-level image and the color field embeddings. Samples from this conditional model result in diverse colorization. We demonstrate that our method obtains better diverse colorizations than a standard conditional variational autoencoder (CVAE) model, as well as a recently proposed conditional generative adversarial network (cGAN).

Connected through hyperedge HE-1 (induced by source paper "an introduction to variational autoencoders"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "isolating sources of disentanglement in variational autoencoders"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "bayesian recurrent neural networks"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "attribute guided sketch generation"):
  with members:
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "instagan instance aware image to image translation"
    - "r btn cross domain face composite and synthesis from limited facial patches"
    - "deformable gans for pose based human image generation"
    - "image generation from sketch constraint using contextual gan"
    - "unsupervised creation of parameterized avatars"
    - "unsupervised diverse colorization via generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "guided image generation with conditional invertible neural networks"):
  with members:
    - "videoflow a flow based generative model for video"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "analyzing inverse problems with invertible neural networks"
    - "training generative reversible networks"
    - "invertible residual networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "excessive invariance causes adversarial vulnerability"

Connected through hyperedge HE-4 (induced by source paper "deep exemplar based video colorization"):
  with members:
    - "the relativistic discriminator a key element missing from standard gan"
    - "deep video color propagation"
    - "learning blind video temporal consistency"
    - "spectral normalization for generative adversarial networks"
    - "switchable temporal propagation network"
    - "deep exemplar based colorization"
    - "the contextual loss for image transformation with non aligned data"
    - "pixel level semantics guided image colorization"

Connected through hyperedge HE-5 (induced by source paper "pixelated semantic colorization"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "deep exemplar based colorization"
    - "pixel level semantics guided image colorization"
    - "tracking emerges by colorizing videos"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "pixcolor pixel recursive colorization"
    - "real time user guided image colorization with learned deep priors"
    - "unsupervised diverse colorization via generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "big but imperceptible adversarial perturbations via semantic manipulation"):
  with members:
    - "spatially transformed adversarial examples"
    - "quantitative evaluation of style transfer"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "exploring the space of black box attacks on deep neural networks"
    - "generating adversarial examples with adversarial networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "convolutional image captioning"

Connected through hyperedge HE-7 (induced by source paper "deep generative models deterministic prediction with an application in inverse rendering"):
  with members:
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "toward controlled generation of text"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved adversarial systems for 3d object generation and reconstruction"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-8 (induced by source paper "fully automatic video colorization with self regularization and diversity"):
  with members:
    - "deep video color propagation"
    - "learning blind video temporal consistency"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "switchable temporal propagation network"
    - "deep exemplar based colorization"
    - "tracking emerges by colorizing videos"
    - "colorization as a proxy task for visual understanding"
    - "real time user guided image colorization with learned deep priors"

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)