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: "structured stochastic variational inference"
  Abstract: Stochastic variational inference makes it possible to approximate posterior distributions induced by large datasets quickly using stochastic optimization. The algorithm relies on the use of fully factorized variational distributions. However, this "mean-field" independence approximation limits the fidelity of the posterior approximation, and introduces local optima. We show how to relax the mean-field approximation to allow arbitrary dependencies between global parameters and local hidden variables, producing better parameter estimates by reducing bias, sensitivity to local optima, and sensitivity to hyperparameters.

Connected through hyperedge HE-1 (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"
    - "variational bayesian inference with stochastic search"

Connected through hyperedge HE-2 (induced by source paper "deep generative model with beta bernoulli process for modeling and learning confounding factors"):
  with members:
    - "deep visual domain adaptation a survey"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "nonparametric variational auto encoders for hierarchical representation learning"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "variational lossy autoencoder"
    - "importance weighted autoencoders"
    - "deep convolutional inverse graphics network"

Connected through hyperedge HE-3 (induced by source paper "admm based networked stochastic variational inference"):
  with members:
    - "streaming distributed variational inference for bayesian nonparametrics"
    - "d mfvi distributed mean field variational inference using bregman admm"
    - "gaussian processes for big data"

Connected through hyperedge HE-4 (induced by source paper "advances in variational inference"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "iterative amortized inference"
    - "inference suboptimality in variational autoencoders"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "faster coordinate descent via adaptive importance sampling"
    - "the numerics of gans"
    - "stein variational policy gradient"

Connected through hyperedge HE-5 (induced by source paper "coordinated multi agent imitation learning"):
  with members:
    - "smooth imitation learning for online sequence prediction"
    - "generative adversarial imitation learning"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "search based structured prediction"

Connected through hyperedge HE-6 (induced by source paper "perturb and mpm quantifying segmentation uncertainty in dense multi label crfs"):
  with members:
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields"
    - "on sampling from the gibbs distribution with random maximum a posteriori perturbations"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-7 (induced by source paper "inter battery topic representation learning"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "factorized topic models"
    - "manifold relevance determination"
    - "representation learning a review and new perspectives"
    - "factorized multi modal topic model"

Connected through hyperedge HE-8 (induced by source paper "online but accurate inference for latent variable models with local gibbs sampling"):
  with members:
    - "streaming gibbs sampling for lda model"
    - "same but different fast and high quality gibbs parameter estimation"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"

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)