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: "eigenvalue corrected noisy natural gradient"
  Abstract: Variational Bayesian neural networks combine the flexibility of deep learning with Bayesian uncertainty estimation. However, inference procedures for flexible variational posteriors are computationally expensive. A recently proposed method, noisy natural gradient, is a surprisingly simple method to fit expressive posteriors by adding weight noise to regular natural gradient updates. Noisy K-FAC is an instance of noisy natural gradient that fits a matrix-variate Gaussian posterior with minor changes to ordinary K-FAC. Nevertheless, a matrix-variate Gaussian posterior does not capture an accurate diagonal variance. In this work, we extend on noisy K-FAC to obtain a more flexible posterior distribution called eigenvalue corrected matrix-variate Gaussian. The proposed method computes the full diagonal re-scaling factor in Kronecker-factored eigenbasis. Empirically, our approach consistently outperforms existing algorithms (e.g., noisy K-FAC) on regression and classification tasks.

Connected through hyperedge HE-1 (induced by source paper "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"):
  with members:
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "understanding short horizon bias in stochastic meta optimization"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "an empirical model of large batch training"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "three mechanisms of weight decay regularization"

Connected through hyperedge HE-2 (induced by source paper "eigendamage structured pruning in the kronecker factored eigenbasis"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "three mechanisms of weight decay regularization"
    - "rethinking the value of network pruning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "noisy natural gradient as variational inference"

Connected through hyperedge HE-3 (induced by source paper "dbsn measuring uncertainty through bayesian learning of deep neural network structures"):
  with members:
    - "functional variational bayesian neural networks"
    - "single path one shot neural architecture search with uniform sampling"
    - "self tuning networks bilevel optimization of hyperparameters using structured best response functions"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"

Connected through hyperedge HE-4 (induced by source paper "the functional neural process"):
  with members:
    - "functional variational bayesian neural networks"
    - "correlated variational auto encoders"
    - "attentive neural processes"
    - "associative compression networks for representation learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "noisy natural gradient as variational inference"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "functional variational bayesian neural networks"):
  with members:
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "differentiable compositional kernel learning for gaussian processes"
    - "scalable gaussian processes with billions of inducing inputs via tensor train decomposition"
    - "noisy natural gradient as variational inference"
    - "amortised map inference for image super resolution"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "fastfood approximate kernel expansions in loglinear time"
    - "an information theoretic analysis of thompson sampling"

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)