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 bayesian encourages dropout"
  Abstract: Dropout is one of the key techniques to prevent the learning from overfitting. It is explained that dropout works as a kind of modified L2 regularization. Here, we shed light on the dropout from Bayesian standpoint. Bayesian interpretation enables us to optimize the dropout rate, which is beneficial for learning of weight parameters and prediction after learning. The experiment result also encourages the optimization of the dropout.

Connected through hyperedge HE-1 (induced by source paper "the implicit and explicit regularization effects of dropout"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "kernel and deep regimes in overparametrized models"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "transformer xl attentive language models beyond a fixed length context"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "implicit regularization in deep matrix factorization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"

Connected through hyperedge HE-2 (induced by source paper "crowd counting with decomposed uncertainty"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "iterative crowd counting"
    - "switching convolutional neural network for crowd counting"
    - "generating high quality crowd density maps using contextual pyramid cnns"

Connected through hyperedge HE-3 (induced by source paper "regularization for deep learning a taxonomy"):
  with members:
    - "proximal backpropagation"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "deep unsupervised learning through spatial contrasting"
    - "adjusting for dropout variance in batch normalization and weight initialization"
    - "synthesizing robust adversarial examples"
    - "an overview of multi task learning in deep neural networks"
    - "densely connected convolutional networks"
    - "trivializing the energy landscape of deep networks"

Connected through hyperedge HE-4 (induced by source paper "bayesgrad explaining predictions of graph convolutional networks"):
  with members:
    - "smoothgrad removing noise by adding noise"
    - "learning important features through propagating activation differences"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-5 (induced by source paper "dropout based active learning for regression"):
  with members:
    - "deep bayesian active learning with image data"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "theano a python framework for fast computation of mathematical expressions"
    - "cltune a generic auto tuner for opencl kernels"
    - "adam a method for stochastic optimization"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "benchmarking optimization software with performance profiles"

Connected through hyperedge HE-6 (induced by source paper "dropout feature ranking for deep learning models"):
  with members:
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "clinical intervention prediction and understanding using deep networks"
    - "learning important features through propagating activation differences"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "why should i trust you explaining the predictions of any classifier"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-7 (induced by source paper "dropout training for svms with data augmentation"):
  with members:
    - "on the inductive bias of dropout"
    - "efficient gradient based inference through transformations between bayes nets and neural nets"
    - "dropout training for support vector machines"
    - "representation learning a review and new perspectives"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "marginalized denoising autoencoders for domain adaptation"
    - "mean field theory for sigmoid belief 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)