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: "tackling over pruning in variational autoencoders"
  Abstract: Variational autoencoders (VAE) are directed generative models that learn factorial latent variables. As noted by Burda et al. (2015), these models exhibit the problem of factor over-pruning where a significant number of stochastic factors fail to learn anything and become inactive. This can limit their modeling power and their ability to learn diverse and meaningful latent representations. In this paper, we evaluate several methods to address this problem and propose a more effective model-based approach called the epitomic variational autoencoder (eVAE). The so-called epitomes of this model are groups of mutually exclusive latent factors that compete to explain the data. This approach helps prevent inactive units since each group is pressured to explain the data. We compare the approaches with qualitative and quantitative results on MNIST and TFD datasets. Our results show that eVAE makes efficient use of model capacity and generalizes better than VAE.

Connected through hyperedge HE-1 (induced by source paper "balancing reconstruction error and kullback leibler divergence in variational autoencoders"):
  with members:
    - "diagnosing and enhancing vae models"
    - "from variational to deterministic autoencoders"
    - "sparsity in variational autoencoders"
    - "fixing a broken elbo"
    - "vae with a vampprior"
    - "improving variational inference with inverse autoregressive flow"
    - "importance weighted autoencoders"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-2 (induced by source paper "the usual suspects reassessing blame for vae posterior collapse"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "diagnosing and enhancing vae models"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "variational autoencoders pursue pca directions by accident"
    - "improving explorability in variational inference with annealed variational objectives"
    - "hidden talents of the variational autoencoder"
    - "fixing a broken elbo"
    - "vae with a vampprior"

Connected through hyperedge HE-3 (induced by source paper "don t blame the elbo a linear vae perspective on posterior collapse"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "diagnosing and enhancing vae models"
    - "preventing posterior collapse with delta vaes"
    - "loss landscapes of regularized linear autoencoders"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "isolating sources of disentanglement in variational autoencoders"
    - "inference suboptimality in variational autoencoders"
    - "variational autoencoders pursue pca directions by accident"

Connected through hyperedge HE-4 (induced by source paper "density estimation and incremental learning of latent vector for generative autoencoders"):
  with members:
    - "vae with a vampprior"
    - "perceptual losses for real time style transfer and super resolution"
    - "neural autoregressive distribution estimation"
    - "density estimation using real nvp"
    - "deep feature consistent variational autoencoder"
    - "wavenet a generative model for raw audio"
    - "made masked autoencoder for distribution estimation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "mu forcing training variational recurrent autoencoders for text generation"):
  with members:
    - "texygen a benchmarking platform for text generation models"
    - "improving variational encoder decoders in dialogue generation"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "infovae information maximizing variational autoencoders"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "fixing a broken elbo"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-6 (induced by source paper "mr image reconstruction using deep density priors"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "image reconstruction by domain transform manifold learning"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deep convolutional neural network for inverse problems in imaging"
    - "bayesian image quality transfer with cnns exploring uncertainty in dmri super resolution"
    - "a survey on deep learning in medical image analysis"
    - "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"
    - "recurrent inference machines for solving inverse problems"

Connected through hyperedge HE-7 (induced by source paper "a rad approach to deep mixture models"):
  with members:
    - "on gans and gmms"
    - "mixture density generative adversarial networks"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "neural discrete representation learning"
    - "density estimation using real nvp"

Connected through hyperedge HE-8 (induced by source paper "assisted sound sample generation with musical conditioning in adversarial auto encoders"):
  with members:
    - "fast spectrogram inversion using multi head convolutional neural networks"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "infovae information maximizing variational autoencoders"
    - "towards deeper understanding of variational autoencoding models"
    - "exploring the structure of a real time arbitrary neural artistic stylization network"
    - "film visual reasoning with a general conditioning layer"
    - "fader networks manipulating images by sliding attributes"

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)