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: "lets keep it simple using simple architectures to outperform deeper and more complex architectures"
  Abstract: Major winning Convolutional Neural Networks (CNNs), such as AlexNet, VGGNet, ResNet, GoogleNet, include tens to hundreds of millions of parameters, which impose considerable computation and memory overhead. This limits their practical use for training, optimization and memory efficiency. On the contrary, light-weight architectures, being proposed to address this issue, mainly suffer from low accuracy. These inefficiencies mostly stem from following an ad hoc procedure. We propose a simple architecture, called SimpleNet, based on a set of designing principles, with which we empirically show, a well-crafted yet simple and reasonably deep architecture can perform on par with deeper and more complex architectures. SimpleNet provides a good tradeoff between the computation/memory efficiency and the accuracy. Our simple 13-layer architecture outperforms most of the deeper and complex architectures to date such as VGGNet, ResNet, and GoogleNet on several well-known benchmarks while having 2 to 25 times fewer number of parameters and operations. This makes it very handy for embedded system or system with computational and memory limitations. We achieved state-of-the-art result on CIFAR10 outperforming several heavier architectures, near state of the art on MNIST and competitive results on CIFAR100 and SVHN. Models are made available at: this https URL

Connected through hyperedge HE-1 (induced by source paper "a branching and merging convolutional network with homogeneous filter capsules"):
  with members:
    - "homogeneous vector capsules enable adaptive gradient descent in convolutional neural networks"
    - "rmdl random multimodel deep learning for classification"
    - "averaging weights leads to wider optima and better generalization"
    - "random erasing data augmentation"
    - "dynamic routing between capsules"
    - "densely connected convolutional networks"
    - "batch normalized maxout network in network"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "3d dynamic hand gestures recognition using the leap motion sensor and convolutional neural networks"):
  with members:
    - "fastai a layered api for deep learning"
    - "on the variance of the adaptive learning rate and beyond"
    - "dynamic gesture recognition by using cnns and star rgb a temporal information condensation"
    - "lookahead optimizer k steps forward 1 step back"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "previous a methodology for prediction of visual inference performance on iot devices"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "design automation for efficient deep learning computing"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "effnet an efficient structure for convolutional neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "benchmark analysis of representative deep neural network architectures"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"

Connected through hyperedge HE-4 (induced by source paper "pruning by explaining a novel criterion for deep neural network pruning"):
  with members:
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "importance estimation for neural network pruning"
    - "compact and computationally efficient representation of deep neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "nisp pruning networks using neuron importance score propagation"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"

Connected through hyperedge HE-5 (induced by source paper "a simple dynamic learning rate tuning algorithm for automated training of dnns"):
  with members:
    - "online learning rate adaptation with hypergradient descent"
    - "don t decay the learning rate increase the batch size"
    - "improved regularization of convolutional neural networks with cutout"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "exploring loss function topology with cyclical learning rates"
    - "sgdr stochastic gradient descent with restarts"
    - "densely connected convolutional networks"
    - "lets keep it simple using simple architectures to outperform deeper architectures"

Connected through hyperedge HE-6 (induced by source paper "machine learning automation toolbox mlaut"):
  with members:
    - "optimizing deep neural network architecture a tabu search based approach"
    - "training feedforward neural networks with standard logistic activations is feasible"
    - "metaheuristic design of feedforward neural networks"
    - "lets keep it simple using simple architectures to outperform deeper architectures"
    - "comparison of 14 different families of classification algorithms on 115 binary datasets"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "caffe convolutional architecture for fast feature embedding"
    - "census data mining and data analysis using weka"

Connected through hyperedge HE-7 (induced by source paper "netscore towards universal metrics for large scale performance analysis of deep neural networks for practical on device edge usage"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "munet a highly compact deep convolutional neural network architecture for real time embedded traffic sign classification"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "squeezenext hardware aware neural network design"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "progressive neural architecture search"
    - "an analysis of deep neural network models for practical applications"

Connected through hyperedge HE-8 (induced by source paper "a new channel boosted convolution neural network using transfer learning"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "smote synthetic minority over sampling technique"

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.CV(Computer Vision and Pattern Recognition)