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: "mean field theory for sigmoid belief networks"
  Abstract: We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the likelihood of evidence. We demonstrate the utility of this framework on a benchmark problem in statistical pattern recognition---the classification of handwritten digits.

Connected through hyperedge HE-1 (induced by source paper "a tutorial on learning with bayesian networks"):
  with members:
    - "query dags a practical paradigm for implementing belief network inference"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "asymptotic model selection for directed networks with hidden variables"
    - "directed reduction algorithms and decomposable graphs"
    - "a transformational characterization of equivalent bayesian network structures"
    - "computing upper and lower bounds on likelihoods in intractable networks"
    - "theory refinement on bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "inductive transfer for neural architecture optimization"):
  with members:
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "darts differentiable architecture search"
    - "resource efficient neural architect"
    - "efficient neural architecture search via parameter sharing"
    - "accelerating neural architecture search using performance prediction"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-3 (induced by source paper "stochastic feedforward neural networks universal approximation"):
  with members:
    - "restricted boltzmann machines introduction and review"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "simplified stochastic feedforward neural networks"
    - "the expressive power of neural networks a view from the width"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "the power of depth for feedforward neural networks"

Connected through hyperedge HE-4 (induced by source paper "amortized inference of variational bounds for learning noisy or"):
  with members:
    - "adam a method for stochastic optimization"
    - "neural variational inference and learning in belief networks"
    - "unsupervised learning of noisy or bayesian networks"
    - "recognition networks for approximate inference in bn20 networks"
    - "variational probabilistic inference and the qmr dt network"

Connected through hyperedge HE-5 (induced by source paper "divide and couple using monte carlo variational objectives for posterior approximation"):
  with members:
    - "importance weighting and variational inference"
    - "using large ensembles of control variates for variational inference"
    - "filtering variational objectives"
    - "fast black box variational inference through stochastic trust region optimization"
    - "importance weighted autoencoders"

Connected through hyperedge HE-6 (induced by source paper "a review of learning with deep generative models from perspective of graphical modeling"):
  with members:
    - "cgans with projection discriminator"
    - "improved training of neural trans dimensional random field language models with dynamic noise contrastive estimation"
    - "the gan landscape losses architectures regularization and normalization"
    - "spectral normalization for generative adversarial networks"
    - "inference suboptimality in variational autoencoders"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "advances in variational inference"
    - "improved variational autoencoders for text modeling using dilated convolutions"

Connected through hyperedge HE-7 (induced by source paper "on the relative expressiveness of bayesian and neural networks"):
  with members:
    - "on relaxing determinism in arithmetic circuits"
    - "human level intelligence or animal like abilities"
    - "bounding and counting linear regions of deep neural networks"
    - "a differential approach to inference in bayesian networks"
    - "making sensitivity analysis computationally efficient"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-8 (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"

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.AI(Artificial Intelligence)