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: "adjusting for dropout variance in batch normalization and weight initialization"
  Abstract: We show how to adjust for the variance introduced by dropout with corrections to weight initialization and Batch Normalization, yielding higher accuracy. Though dropout can preserve the expected input to a neuron between train and test, the variance of the input differs. We thus propose a new weight initialization by correcting for the influence of dropout rates and an arbitrary nonlinearity's influence on variance through simple corrective scalars. Since Batch Normalization trained with dropout estimates the variance of a layer's incoming distribution with some inputs dropped, the variance also differs between train and test. After training a network with Batch Normalization and dropout, we simply update Batch Normalization's variance moving averages with dropout off and obtain state of the art on CIFAR-10 and CIFAR-100 without data augmentation.

Connected through hyperedge HE-1 (induced by source paper "understanding the disharmony between dropout and batch normalization by variance shift"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "self normalizing neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"
    - "aggregated residual transformations for deep neural networks"
    - "learning from simulated and unsupervised images through adversarial training"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "fire ssd wide fire modules based single shot detector on edge device"):
  with members:
    - "feature fused ssd fast detection for small objects"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "enhancement of ssd by concatenating feature maps for object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dssd deconvolutional single shot detector"
    - "dsod learning deeply supervised object detectors from scratch"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "accurate large minibatch sgd training imagenet in 1 hour"

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"
    - "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"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-4 (induced by source paper "sunlayer stable denoising with generative networks"):
  with members:
    - "the sample complexity of multi reference alignment"
    - "swish a self gated activation function"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "perceptual losses for real time style transfer and super resolution"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-5 (induced by source paper "bridging nonlinearities and stochastic regularizers with gaussian error linear units"):
  with members:
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "sgdr stochastic gradient descent with restarts"
    - "deep networks with stochastic depth"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "wide residual networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "all you need is a good init"

Connected through hyperedge HE-6 (induced by source paper "a baseline for detecting misclassified and out of distribution examples in neural networks"):
  with members:
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "sgdr stochastic gradient descent with restarts"
    - "unsupervised risk estimation using only conditional independence structure"
    - "posterior calibration and exploratory analysis for natural language processing models"
    - "thchs 30 a free chinese speech corpus"
    - "adam a method for stochastic optimization"
    - "thumbs up sentiment classification using machine learning techniques"

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)