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 architectures"
  Abstract: In recent years, all winning architectures that achieved state of the art results have been very deep and had parameters ranging from tens to hundreds of millions. While an optimal depth has yet to be discovered, it is known that these deep architectures are far from being optimal. Very deep and heavy architecture such as VGGNet, GoogleNet, ResNet and the likes, are very demanding in terms of hardware requirements, and so their practical use has become very limited and costly. The computational and memory overhead caused by such architectures has a negative effect on the expansion of methods and applications utilizing deep architectures. In order to overcome this issues, some thinned architectures such as Squeezenet are proposed that are computationally light-weight and useful for embedded systems. However their usage is also hindered by the low accuracy they provide. While deep architectures do provide good accuracy, we empirically show that a well-crafted yet simple and reasonably deep architecture can equally perform. This allows for more practical uses, especially in embed systems, or systems with computational and memory limitations. In this work, we present a very simple fully convolutional network with 13 layers that outperforms almost all deeper architectures to date such as ResNet, GoogleNet,WRN, etc with 2 to 25 times fewer number of parameters, and rarely when it does not supersede an architecture, it performs on par. We achieved state of the art results and very close to it on datasets such as CIFAR10/100, MNIST and SVHN with simple or no data-augmentation.

Connected through hyperedge HE-1 (induced by source paper "a framework for large scale mapping of human settlement extent from sentinel 2 images via fully convolutional neural networks"):
  with members:
    - "sen12ms a curated dataset of georeferenced multi spectral sentinel 1 2 imagery for deep learning and data fusion"
    - "relation network for multi label aerial image classification"
    - "so2sat lcz42 a benchmark dataset for global local climate zones classification"
    - "bigearthnet a large scale benchmark archive for remote sensing image understanding"
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "mapping missing population in rural india a deep learning approach with satellite imagery"
    - "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification"
    - "cospace common subspace learning from hyperspectral multispectral correspondences"

Connected through hyperedge HE-2 (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-3 (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"
    - "lets keep it simple using simple architectures to outperform deeper and more complex architectures"
    - "densely connected convolutional networks"

Connected through hyperedge HE-4 (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 and more complex 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"

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)