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: "neural variational inference and learning in belief networks"
  Abstract: Highly expressive directed latent variable models, such as sigmoid belief networks, are difficult to train on large datasets because exact inference in them is intractable and none of the approximate inference methods that have been applied to them scale well. We propose a fast non-iterative approximate inference method that uses a feedforward network to implement efficient exact sampling from the variational posterior. The model and this inference network are trained jointly by maximizing a variational lower bound on the log-likelihood. Although the naive estimator of the inference model gradient is too high-variance to be useful, we make it practical by applying several straightforward model-independent variance reduction techniques. Applying our approach to training sigmoid belief networks and deep autoregressive networks, we show that it outperforms the wake-sleep algorithm on MNIST and achieves state-of-the-art results on the Reuters RCV1 document dataset.

Connected through hyperedge HE-1 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement"):
  with members:
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "direct policy gradients direct optimization of policies in discrete action spaces"
    - "direct optimization through arg max for discrete variational auto encoder"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "neural machine translation with gumbel greedy decoding"
    - "classical structured prediction losses for sequence to sequence learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "vq draw a sequential discrete vae"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "generating diverse high fidelity images with vq vae 2"
    - "group normalization"
    - "representation learning with contrastive predictive coding"
    - "progressive growing of gans for improved quality stability and variation"
    - "neural discrete representation learning"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"

Connected through hyperedge HE-4 (induced by source paper "generalized gumbel softmax gradient estimator for various discrete random variables"):
  with members:
    - "estimating gradients for discrete random variables by sampling without replacement"
    - "implicit reparameterization gradients"
    - "dirichlet variational autoencoder"
    - "discovering discrete latent topics with neural variational inference"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "tensorflow a system for large scale machine learning"
    - "neural variational inference for text processing"
    - "muprop unbiased backpropagation for stochastic neural networks"

Connected through hyperedge HE-5 (induced by source paper "variational item response theory fast accurate and expressive"):
  with members:
    - "the stanford acuity test a probabilistic approach for precise visual acuity testing"
    - "deep irt make deep learning based knowledge tracing explainable using item response theory"
    - "neural autoregressive flows"
    - "pyro deep universal probabilistic programming"
    - "multimodal generative models for scalable weakly supervised learning"
    - "attention is all you need"
    - "improving variational auto encoders using householder flow"
    - "dynamic key value memory networks for knowledge tracing"

Connected through hyperedge HE-6 (induced by source paper "try depth instead of weight correlations mean field is a less restrictive assumption for deeper networks"):
  with members:
    - "functional variational bayesian neural networks"
    - "on the convergence of adam and beyond"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep residual learning for image recognition"
    - "nonparametric variational inference"

Connected through hyperedge HE-7 (induced by source paper "unsupervised recurrent neural network grammars"):
  with members:
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "cooperative learning of disjoint syntax and semantics"
    - "finding syntax in human encephalography with beam search"
    - "on tree based neural sentence modeling"
    - "unsupervised grammar induction with depth bounded pcfg"
    - "backpropagating through structured argmax using a spigot"
    - "inducing grammars with and for neural machine translation"
    - "latent tree learning with differentiable parsers shift reduce parsing and chart parsing"

Connected through hyperedge HE-8 (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"

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)