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 bayesian inference with stochastic search"
  Abstract: Mean-field variational inference is a method for approximate Bayesian posterior inference. It approximates a full posterior distribution with a factorized set of distributions by maximizing a lower bound on the marginal likelihood. This requires the ability to integrate a sum of terms in the log joint likelihood using this factorized distribution. Often not all integrals are in closed form, which is typically handled by using a lower bound. We present an alternative algorithm based on stochastic optimization that allows for direct optimization of the variational lower bound. This method uses control variates to reduce the variance of the stochastic search gradient, in which existing lower bounds can play an important role. We demonstrate the approach on two non-conjugate models: logistic regression and an approximation to the HDP.

Connected through hyperedge HE-1 (induced by source paper "towards a kernel based physical interpretation of model uncertainty"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "latent projection bnns avoiding weight space pathologies by learning latent representations of neural network weights"
    - "deep exploration via bootstrapped dqn"

Connected through hyperedge HE-2 (induced by source paper "gasl guided attention for sparsity learning in deep neural networks"):
  with members:
    - "attention is all you need"
    - "learning structured sparsity in deep neural networks"
    - "attention correctness in neural image captioning"
    - "neural machine translation with supervised attention"
    - "guided alignment training for topic aware neural machine translation"
    - "supervised attentions for neural machine translation"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-3 (induced by source paper "moped efficient priors for scalable variational inference in bayesian deep neural networks"):
  with members:
    - "functional variational bayesian neural networks"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-4 (induced by source paper "random function priors for correlation modeling"):
  with members:
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "structured stochastic variational inference"

Connected through hyperedge HE-5 (induced by source paper "direct optimization through arg max for discrete variational auto encoder"):
  with members:
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "dvae discrete variational autoencoders with overlapping transformations"
    - "toward controlled generation of text"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "learning hard alignments with variational inference"

Connected through hyperedge HE-6 (induced by source paper "backprop q generalized backpropagation for stochastic computation graphs"):
  with members:
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "decoupled neural interfaces using synthetic gradients"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "q prop sample efficient policy gradient with an off policy critic"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-7 (induced by source paper "learning a representation map for robot navigation using deep variational autoencoder"):
  with members:
    - "a hybrid convolutional variational autoencoder for text generation"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "variational lossy autoencoder"
    - "attribute2image conditional image generation from visual attributes"
    - "deep convolutional inverse graphics network"
    - "autoencoding beyond pixels using a learned similarity metric"

Connected through hyperedge HE-8 (induced by source paper "deep ensemble bayesian active learning addressing the mode collapse issue in monte carlo dropout via ensembles"):
  with members:
    - "adversarial active learning for deep networks a margin based approach"
    - "deep active learning for named entity recognition"
    - "active learning for visual question answering an empirical study"
    - "deep bayesian active learning with image data"
    - "active and continuous exploration with deep neural networks and expected model output changes"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"

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)