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: "gradual dropin of layers to train very deep neural networks"
  Abstract: We introduce the concept of dynamically growing a neural network during training. In particular, an untrainable deep network starts as a trainable shallow network and newly added layers are slowly, organically added during training, thereby increasing the network's depth. This is accomplished by a new layer, which we call DropIn. The DropIn layer starts by passing the output from a previous layer (effectively skipping over the newly added layers), then increasingly including units from the new layers for both feedforward and backpropagation. We show that deep networks, which are untrainable with conventional methods, will converge with DropIn layers interspersed in the architecture. In addition, we demonstrate that DropIn provides regularization during training in an analogous way as dropout. Experiments are described with the MNIST dataset and various expanded LeNet architectures, CIFAR-10 dataset with its architecture expanded from 3 to 11 layers, and on the ImageNet dataset with the AlexNet architecture expanded to 13 layers and the VGG 16-layer architecture.

Connected through hyperedge HE-1 (induced by source paper "effective training of convolutional neural networks with low bitwidth weights and activations"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "single path one shot neural architecture search with uniform sampling"
    - "precision highway for ultra low precision quantization"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-2 (induced by source paper "autogrow automatic layer growing in deep convolutional networks"):
  with members:
    - "cgap continuous growth and pruning for efficient deep learning"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "evolving deep neural networks"
    - "progressive neural architecture search"
    - "learning efficient convolutional networks through network slimming"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-3 (induced by source paper "training quantized network with auxiliary gradient module"):
  with members:
    - "precision highway for ultra low precision quantization"
    - "slimmable neural networks"
    - "model compression via distillation and quantization"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "proxquant quantized neural networks via proximal operators"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "relaxed quantization for discretized neural networks"

Connected through hyperedge HE-4 (induced by source paper "effective and efficient dropout for deep convolutional neural networks"):
  with members:
    - "model slicing for supporting complex analytics with elastic inference cost and resource constraints"
    - "group normalization"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"
    - "noscope optimizing neural network queries over video at scale"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-5 (induced by source paper "regularizing neural networks via stochastic branch layers"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep networks with stochastic depth"
    - "improved dropout for shallow and deep learning"
    - "dropout with expectation linear regularization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "accudnn a gpu memory efficient accelerator for training ultra deep neural networks"):
  with members:
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "tflms large model support in tensorflow by graph rewriting"
    - "don t decay the learning rate increase the batch size"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "training deep nets with sublinear memory cost"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-7 (induced by source paper "few sample knowledge distillation for efficient network compression"):
  with members:
    - "knowledge transfer with jacobian matching"
    - "network decoupling from regular to depthwise separable convolutions"
    - "learning efficient convolutional networks through network slimming"
    - "deep mutual learning"
    - "thinet a filter level pruning method for deep neural network compression"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "data free knowledge distillation for deep neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-8 (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"

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.NE(Neural and Evolutionary Computing)