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: "towards deeper understanding of variational autoencoding models"
  Abstract: We propose a new family of optimization criteria for variational auto-encoding models, generalizing the standard evidence lower bound. We provide conditions under which they recover the data distribution and learn latent features, and formally show that common issues such as blurry samples and uninformative latent features arise when these conditions are not met. Based on these new insights, we propose a new sequential VAE model that can generate sharp samples on the LSUN image dataset based on pixel-wise reconstruction loss, and propose an optimization criterion that encourages unsupervised learning of informative latent features.

Connected through hyperedge HE-1 (induced by source paper "hierarchical quantized autoencoders"):
  with members:
    - "exact rate distortion in autoencoders via echo noise"
    - "on the variance of the adaptive learning rate and beyond"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "rethinking lossy compression the rate distortion perception tradeoff"
    - "the perception distortion tradeoff"
    - "deep generative models for distribution preserving lossy compression"
    - "recent advances in autoencoder based representation learning"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"

Connected through hyperedge HE-2 (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"
    - "vae with a vampprior"
    - "neural discrete representation learning"
    - "pixelvae a latent variable model for natural images"

Connected through hyperedge HE-3 (induced by source paper "multimodal generative models for compositional representation learning"):
  with members:
    - "factorized inference in deep markov models for incomplete multimodal time series"
    - "measuring compositionality in representation learning"
    - "vae gans for probabilistic compressive image recovery uncertainty analysis"
    - "shapeglot learning language for shape differentiation"
    - "neural autoregressive flows"
    - "graph r cnn for scene graph generation"
    - "yolov3 an incremental improvement"
    - "multimodal matching using a hybrid convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "from variational to deterministic autoencoders"):
  with members:
    - "diagnosing and enhancing vae models"
    - "generating diverse high fidelity images with vq vae 2"
    - "the gan landscape losses architectures regularization and normalization"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "spectral normalization for generative adversarial networks"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "large scale gan training for high fidelity natural image synthesis"
    - "resisting adversarial attacks using gaussian mixture variational autoencoders"

Connected through hyperedge HE-5 (induced by source paper "inverse graphics unsupervised learning of 3d shapes from single images"):
  with members:
    - "bridging the elbo and mmd"
    - "preventing posterior collapse with delta vaes"
    - "structure aware shape synthesis"
    - "visual object networks image generation with disentangled 3d representation"
    - "learning category specific mesh reconstruction from image collections"
    - "3d lmnet latent embedding matching for accurate and diverse 3d point cloud reconstruction from a single image"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "3d prnn generating shape primitives with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "intrinsically motivated discovery of diverse patterns in self organizing systems"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "skew fit state covering self supervised reinforcement learning"
    - "exploration by random network distillation"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "large scale study of curiosity driven learning"
    - "isolating sources of disentanglement in variational autoencoders"
    - "curiosity driven exploration of learned disentangled goal spaces"

Connected through hyperedge HE-7 (induced by source paper "active image synthesis for efficient labeling"):
  with members:
    - "adversarial active learning for deep networks a margin based approach"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "generative invertible networks gin pathophysiology interpretable feature mapping and virtual patient generation"
    - "generative adversarial active learning"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"
    - "deep bayesian active learning with image data"

Connected through hyperedge HE-8 (induced by source paper "noise contrastive variational autoencoders"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "contrastive variational autoencoder enhances salient features"
    - "preventing posterior collapse with delta vaes"
    - "mae mutual posterior divergence regularization for variational autoencoders"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "bounded information rate variational autoencoders"
    - "infovae information maximizing variational autoencoders"
    - "improved variational autoencoders for text modeling using dilated convolutions"

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)