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: "fixing variational bayes deterministic variational inference for bayesian neural networks"
  Abstract: Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probabilistic inference in deep neural networks, variational Bayes (VB) is theoretically grounded, generally applicable, and computationally efficient. With wide recognition of potential advantages, why is it that variational Bayes has seen very limited practical use for BNNs in real applications? We argue that variational inference in neural networks is fragile: successful implementations require careful initialization and tuning of prior variances, as well as controlling the variance of Monte Carlo gradient estimates. We fix VB and turn it into a robust inference tool for Bayesian neural networks. We achieve this with two innovations: first, we introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel empirical Bayes procedure for automatically selecting prior variances. Combining these two innovations, the resulting method is highly efficient and robust. On the application of heteroscedastic regression we demonstrate strong predictive performance over alternative approaches.

Connected through hyperedge HE-1 (induced by source paper "a simple baseline for bayesian uncertainty in deep learning"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "swalp stochastic weight averaging in low precision training"
    - "subspace inference for bayesian deep learning"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "averaging weights leads to wider optima and better generalization"
    - "gradient descent happens in a tiny subspace"

Connected through hyperedge HE-2 (induced by source paper "calibration of deep probabilistic models with decoupled bayesian neural networks"):
  with members:
    - "dirichlet based gaussian processes for large scale calibrated classification"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning for single shot confidence calibration in deep neural networks through stochastic inferences"
    - "confidence scoring using whitebox meta models with linear classifier probes"
    - "ergodic measure preserving flows"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "inference suboptimality in variational autoencoders"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-3 (induced by source paper "sampling free learning of bayesian quantized neural networks"):
  with members:
    - "a comprehensive guide to bayesian convolutional neural network with variational inference"
    - "lightweight probabilistic deep networks"
    - "probabilistic binary neural networks"
    - "learning discrete weights using the local reparameterization trick"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "bitwise neural networks"

Connected through hyperedge HE-4 (induced by source paper "better pac bayes bounds for deep neural networks using the loss curvature"):
  with members:
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "limitations of the empirical fisher approximation"
    - "information matrices and generalization"
    - "the information complexity of learning tasks their structure and their distance"
    - "some limitations of norm based generalization bounds in deep neural networks"
    - "data dependent pac bayes priors via differential privacy"
    - "fisher rao metric geometry and complexity of neural networks"

Connected through hyperedge HE-5 (induced by source paper "subspace inference for bayesian deep learning"):
  with members:
    - "quality of uncertainty quantification for bayesian neural network inference"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "measuring the intrinsic dimension of objective landscapes"
    - "pyro deep universal probabilistic programming"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "averaging weights leads to wider optima and better generalization"
    - "gradient descent happens in a tiny subspace"
    - "latent projection bnns avoiding weight space pathologies by learning latent representations of neural network weights"

Connected through hyperedge HE-6 (induced by source paper "efficient evaluation time uncertainty estimation by improved distillation"):
  with members:
    - "reducing overconfident errors outside the known distribution"
    - "discrimination aware channel pruning for deep neural networks"
    - "large scale distributed neural network training through online distillation"
    - "dropout distillation for efficiently estimating model confidence"
    - "on calibration of modern neural networks"
    - "snapshot ensembles train 1 get m for free"
    - "selective classification for deep neural networks"
    - "identity mappings in deep residual 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)