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: "a comprehensive guide to bayesian convolutional neural network with variational inference"
  Abstract: Artificial Neural Networks are connectionist systems that perform a given task by learning on examples without having prior knowledge about the task. This is done by finding an optimal point estimate for the weights in every node. Generally, the network using point estimates as weights perform well with large datasets, but they fail to express uncertainty in regions with little or no data, leading to overconfident decisions. #R##N#In this paper, Bayesian Convolutional Neural Network (BayesCNN) using Variational Inference is proposed, that introduces probability distribution over the weights. Furthermore, the proposed BayesCNN architecture is applied to tasks like Image Classification, Image Super-Resolution and Generative Adversarial Networks. The results are compared to point-estimates based architectures on MNIST, CIFAR-10 and CIFAR-100 datasets for Image CLassification task, on BSD300 dataset for Image Super Resolution task and on CIFAR10 dataset again for Generative Adversarial Network task. #R##N#BayesCNN is based on Bayes by Backprop which derives a variational approximation to the true posterior. We, therefore, introduce the idea of applying two convolutional operations, one for the mean and one for the variance. Our proposed method not only achieves performances equivalent to frequentist inference in identical architectures but also incorporate a measurement for uncertainties and regularisation. It further eliminates the use of dropout in the model. Moreover, we predict how certain the model prediction is based on the epistemic and aleatoric uncertainties and empirically show how the uncertainty can decrease, allowing the decisions made by the network to become more deterministic as the training accuracy increases. Finally, we propose ways to prune the Bayesian architecture and to make it more computational and time effective.

Connected through hyperedge HE-1 (induced by source paper "a financial service chatbot based on deep bidirectional transformers"):
  with members:
    - "selectivenet a deep neural network with an integrated reject option"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "an evaluation dataset for intent classification and out of scope prediction"
    - "roberta a robustly optimized bert pretraining approach"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "sampling based bayesian inference with gradient uncertainty"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "bayesian neural networks with maximum mean discrepancy regularization"):
  with members:
    - "aleatoric and epistemic uncertainty in machine learning a tutorial introduction"
    - "on calibration of modern neural networks"
    - "infovae information maximizing variational autoencoders"
    - "fixing a broken elbo"
    - "mmd gan towards deeper understanding of moment matching network"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "a wavenet for speech denoising"
    - "generative moment matching networks"

Connected through hyperedge HE-3 (induced by source paper "sampling free learning of bayesian quantized neural networks"):
  with members:
    - "lightweight probabilistic deep networks"
    - "probabilistic binary neural networks"
    - "fixing variational bayes deterministic variational inference for bayesian 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 "probact a probabilistic activation function for deep neural networks"):
  with members:
    - "the quest for the golden activation function"
    - "searching for activation functions"
    - "swish a self gated activation function"
    - "towards robust neural networks via random self ensemble"
    - "noisy activation functions"
    - "parametric exponential linear unit for deep convolutional neural networks"
    - "deep learning in neural networks"
    - "deep learning with s shaped rectified linear activation units"

Connected through hyperedge HE-5 (induced by source paper "variational resampling based assessment of deep neural networks under distribution shift"):
  with members:
    - "adversarial invariant feature learning with accuracy constraint for domain generalization"
    - "tutorial and survey on probabilistic graphical model and variational inference in deep reinforcement learning"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "high dimensional restrictive federated model selection with multi objective bayesian optimization over shifted distributions"
    - "reinbo machine learning pipeline search and configuration with bayesian optimization embedded reinforcement learning"
    - "rethinking clinical prediction why machine learning must consider year of care and feature aggregation"
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"

Connected through hyperedge HE-6 (induced by source paper "towards calibrated and scalable uncertainty representations for neural networks"):
  with members:
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "sampling based bayesian inference with gradient uncertainty"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "on calibration of modern neural networks"
    - "a survey on deep learning in medical image analysis"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "sgdr stochastic gradient descent with restarts"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-7 (induced by source paper "resampling based assessment of robustness to distribution shift for deep neural networks"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "using pre training can improve model robustness and uncertainty"
    - "high dimensional restrictive federated model selection with multi objective bayesian optimization over shifted distributions"
    - "rethinking clinical prediction why machine learning must consider year of care and feature aggregation"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-8 (induced by source paper "a comparative study between bayesian and frequentist neural networks for remaining useful life estimation in condition based maintenance"):
  with members:
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "temporal convolutional memory networks for remaining useful life estimation of industrial machinery"
    - "a survey on data collection for machine learning a big data ai integration perspective"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "semi supervised learning with deep generative models for asset failure prediction"
    - "predicting remaining useful life using time series embeddings based on recurrent neural networks"
    - "normalization a preprocessing stage"

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)