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: "variational lossy autoencoder"
  Abstract: Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture. In this paper, we present a simple but principled method to learn such global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN. Our proposed VAE model allows us to have control over what the global latent code can learn and , by designing the architecture accordingly, we can force the global latent code to discard irrelevant information such as texture in 2D images, and hence the VAE only "autoencodes" data in a lossy fashion. In addition, by leveraging autoregressive models as both prior distribution $p(z)$ and decoding distribution $p(x|z)$, we can greatly improve generative modeling performance of VAEs, achieving new state-of-the-art results on MNIST, OMNIGLOT and Caltech-101 Silhouettes density estimation tasks.

Connected through hyperedge HE-1 (induced by source paper "latent normalizing flows for many to many cross domain mappings"):
  with members:
    - "sequential latent spaces for modeling the intention during diverse image captioning"
    - "preventing posterior collapse with delta vaes"
    - "mirrorgan learning text to image generation by redescription"
    - "conditional flow variational autoencoders for structured sequence prediction"
    - "m3d gan multi modal multi domain translation with universal attention"
    - "semantics disentangling for text to image generation"
    - "describing like humans on diversity in image captioning"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"

Connected through hyperedge HE-2 (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-3 (induced by source paper "varmixup exploiting the latent space for robust training and inference"):
  with members:
    - "adversarial domain adaptation with domain mixup"
    - "mixup inference better exploiting mixup to defend adversarial attacks"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "harnessing the vulnerability of latent layers in adversarially trained models"
    - "mixmatch a holistic approach to semi supervised learning"
    - "on evaluating adversarial robustness"

Connected through hyperedge HE-4 (induced by source paper "learning cross domain generalizable features by representation disentanglement"):
  with members:
    - "a two step disentanglement method"
    - "domain agnostic learning with disentangled representations"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "semi supervised domain adaptation via minimax entropy"
    - "domain generalization via model agnostic learning of semantic features"
    - "mixmatch a holistic approach to semi supervised learning"
    - "representation disentanglement for multi task learning with application to fetal ultrasound"

Connected through hyperedge HE-5 (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-6 (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-7 (induced by source paper "unified multi domain learning and data imputation using adversarial autoencoder"):
  with members:
    - "gain missing data imputation using generative adversarial nets"
    - "handling incomplete heterogeneous data using vaes"
    - "evolutionary architecture search for deep multitask networks"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unified deep supervised domain adaptation and generalization"
    - "vae with a vampprior"
    - "a survey on multi task learning"

Connected through hyperedge HE-8 (induced by source paper "deterministic decoding for discrete data in variational autoencoders"):
  with members:
    - "from variational to deterministic autoencoders"
    - "a prior of a googol gaussians a tensor ring induced prior for generative models"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "junction tree variational autoencoder for molecular graph generation"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "vae with a vampprior"

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)