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: "towards better exploiting convolutional neural networks for remote sensing scene classification"
  Abstract: Abstract   We present an analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets or CNNs) in different scenarios from the ones they were trained: full training, fine tuning, and using ConvNets as feature extractors. In many applications, especially including remote sensing, it is not feasible to fully design and train a new ConvNet, as this usually requires a considerable amount of labeled data and demands high computational costs. Therefore, it is important to understand how to better use existing ConvNets. We perform experiments with six popular ConvNets using three remote sensing datasets. We also compare ConvNets in each strategy with existing descriptors and with state-of-the-art baselines. Results point that fine tuning tends to be the best performing strategy. In fact, using the features from the fine-tuned ConvNet with linear SVM obtains the best results. We also achieved state-of-the-art results for the three datasets used.

Connected through hyperedge HE-1 (induced by source paper "convolution neural network architecture learning for remote sensing scene classification"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "remote sensing image scene classification benchmark and state of the art"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (induced by source paper "relation network for multi label aerial image classification"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "understanding urban landuse from the above and ground perspectives a deep learning multimodal solution"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "recent advances and opportunities in scene classification of aerial images with deep models"
    - "vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network"
    - "rifcn recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images"
    - "fine grained object recognition and zero shot learning in remote sensing imagery"
    - "im2height height estimation from single monocular imagery via fully residual convolutional deconvolutional network"

Connected through hyperedge HE-3 (induced by source paper "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"):
  with members:
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network"
    - "rifcn recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images"
    - "im2height height estimation from single monocular imagery via fully residual convolutional deconvolutional network"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"

Connected through hyperedge HE-4 (induced by source paper "dynamic multicontext segmentation of remote sensing images based on convolutional networks"):
  with members:
    - "exploiting convnet diversity for flooding identification"
    - "deep learning in remote sensing a review"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"
    - "a survey of semantic segmentation"
    - "classification with an edge improving semantic image segmentation with boundary detection"

Connected through hyperedge HE-5 (induced by source paper "in domain representation learning for remote sensing"):
  with members:
    - "the visual task adaptation benchmark"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "domain adaptive transfer learning with specialist models"
    - "exploring the limits of weakly supervised pretraining"
    - "marta gans unsupervised representation learning for remote sensing image classification"
    - "comprehensive survey of deep learning in remote sensing theories tools and challenges for the community"
    - "filmy cloud removal on satellite imagery with multispectral conditional generative adversarial nets"

Connected through hyperedge HE-6 (induced by source paper "hydra an ensemble of convolutional neural networks for geospatial land classification"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "functional map of the world"
    - "remote sensing image scene classification benchmark and state of the art"
    - "xception deep learning with depthwise separable convolutions"
    - "understanding data augmentation for classification when to warp"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "global temporal representation based cnns for infrared action recognition"):
  with members:
    - "transferable feature representation for visible to infrared cross dataset human action recognition"
    - "leveraging structural context models and ranking score fusion for human interaction prediction"
    - "learning spatiotemporal features for infrared action recognition with 3d convolutional neural networks"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "convolutional two stream network fusion for video action recognition"
    - "long term temporal convolutions for action recognition"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "an introduction to deep morphological networks"):
  with members:
    - "dynamic multicontext segmentation of remote sensing images based on convolutional networks"
    - "an interactive medical image segmentation framework using iterative refinement"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "xception deep learning with depthwise separable convolutions"
    - "visual saliency detection based on multiscale deep cnn features"
    - "deep residual learning for image recognition"
    - "fully convolutional networks for semantic segmentation"

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)