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: "pixelvae a latent variable model for natural images"
  Abstract: Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and model global structure well but have difficulty capturing small details. PixelCNN models details very well, but lacks a latent code and is difficult to scale for capturing large structures. We present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. Our model requires very few expensive autoregressive layers compared to PixelCNN and learns latent codes that are more compressed than a standard VAE while still capturing most non-trivial structure. Finally, we extend our model to a hierarchy of latent variables at different scales. Our model achieves state-of-the-art performance on binarized MNIST, competitive performance on 64x64 ImageNet, and high-quality samples on the LSUN bedrooms dataset.

Connected through hyperedge HE-1 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models"):
  with members:
    - "set flow a permutation invariant normalizing flow"
    - "mintnet building invertible neural networks with masked convolutions"
    - "hybrid models with deep and invertible features"
    - "emerging convolutions for generative normalizing flows"
    - "improving exploration in soft actor critic with normalizing flows policies"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "stochastic neural network with kronecker flow"
    - "invertible residual networks"

Connected through hyperedge HE-2 (induced by source paper "adaptive density estimation for generative models"):
  with members:
    - "improving generalization and stability of generative adversarial networks"
    - "cgans with projection discriminator"
    - "how good is my gan"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "fast generation for convolutional autoregressive models"

Connected through hyperedge HE-3 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling"):
  with members:
    - "object driven text to image synthesis via adversarial training"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "text adaptive generative adversarial networks manipulating images with natural language"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "recent advances in zero shot recognition"
    - "self supervised learning of visual features through embedding images into text topic spaces"

Connected through hyperedge HE-4 (induced by source paper "twostreamvan improving motion modeling in video generation"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "stochastic adversarial video prediction"
    - "probabilistic video generation using holistic attribute control"
    - "stochastic video generation with a learned prior"
    - "a style based generator architecture for generative adversarial networks"
    - "on convergence and stability of gans"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-5 (induced by source paper "learning robust representations via multi view information bottleneck"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "on mutual information maximization for representation learning"
    - "contrastive multiview coding"
    - "on variational bounds of mutual information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "deep variational canonical correlation analysis"

Connected through hyperedge HE-6 (induced by source paper "from abstract items to latent spaces to observed data and back compositional variational auto encoder"):
  with members:
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "deep learning for generic object detection a survey"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "emergence of invariance and disentanglement in deep representations"
    - "neural message passing for quantum chemistry"
    - "opening the black box of deep neural networks via information"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"

Connected through hyperedge HE-7 (induced by source paper "characterizing and avoiding problematic global optima of variational autoencoders"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "preventing posterior collapse with delta vaes"
    - "constrained graph variational autoencoders for molecule design"
    - "infovae information maximizing variational autoencoders"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "towards deeper understanding of variational autoencoding models"
    - "vae with a vampprior"
    - "neural discrete representation learning"

Connected through hyperedge HE-8 (induced by source paper "variational auto encoder not all failures are equal"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "improving explorability in variational inference with annealed variational objectives"
    - "fixing a broken elbo"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "autoencoding beyond pixels using a learned similarity metric"
    - "semi supervised learning with deep generative models"

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.LG(Machine Learning)