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: "3 d deep learning approach for remote sensing image classification"
  Abstract: Recently, a variety of approaches have been enriching the field of remote sensing (RS) image processing and analysis. Unfortunately, existing methods remain limited to the rich spatiospectral content of today’s large data sets. It would seem intriguing to resort to deep learning (DL)-based approaches at this stage with regard to their ability to offer accurate semantic interpretation of the data. However, the specificity introduced by the coexistence of spectral and spatial content in the RS data sets widens the scope of the challenges presented to adapt DL methods to these contexts. Therefore, the aim of this paper is first to explore the performance of DL architectures for the RS hyperspectral data set classification and second to introduce a new 3-D DL approach that enables a joint spectral and spatial information process. A set of 3-D schemes is proposed and evaluated. Experimental results based on well-known hyperspectral data sets demonstrate that the proposed method is able to achieve a better classification rate than state-of-the-art methods with lower computational costs.

Connected through hyperedge HE-1 (induced by source paper "learning hyperspectral feature extraction and classification with resnext network"):
  with members:
    - "lisht non parametric linearly scaled hyperbolic tangent activation function for neural networks"
    - "dual cnn models for unsupervised monocular depth estimation"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "rccnet an efficient convolutional neural network for histological routine colon cancer nuclei classification"
    - "mask r cnn"
    - "aggregated residual transformations for deep neural networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "deep learning for classification of hyperspectral data a comparative review"):
  with members:
    - "low shot learning for the semantic segmentation of remote sensing imagery"
    - "gmm based synthetic samples for classification of hyperspectral images with limited training data"
    - "hsi cnn a novel convolution neural network for hyperspectral image"
    - "generative adversarial networks for realistic synthesis of hyperspectral samples"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "endnet sparse autoencoder network for endmember extraction and hyperspectral unmixing"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "hybridsn exploring 3d 2d cnn feature hierarchy for hyperspectral image classification"):
  with members:
    - "lisht non parametric linearly scaled hyperbolic tangent activation function for neural networks"
    - "dual cnn models for unsupervised monocular depth estimation"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "mask r cnn"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"

Connected through hyperedge HE-4 (induced by source paper "1d convolutional capsule network for hyperspectral image classification"):
  with members:
    - "fast capsnet for lung cancer screening"
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "spectral spatial classification of hyperspectral images three tricks and a new learning setting"
    - "dynamic routing between capsules"
    - "a convolutional neural network for modelling sentences"
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"

Connected through hyperedge HE-5 (induced by source paper "3d cnn with localized residual connections for hyperspectral image classification"):
  with members:
    - "avdnet a small sized vehicle detection network for aerial visual data"
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "contextual deep cnn based hyperspectral classification"

Connected through hyperedge HE-6 (induced by source paper "segmenting hyperspectral images using spectral spatial convolutional neural networks with training time data augmentation"):
  with members:
    - "validating hyperspectral image segmentation"
    - "unsupervised segmentation of hyperspectral images using 3d convolutional autoencoders"
    - "bass net band adaptive spectral spatial feature learning neural network for hyperspectral image classification"

Connected through hyperedge HE-7 (induced by source paper "temporal convolutional neural network for the classification of satellite image time series"):
  with members:
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "deep learning for time series classification a review"
    - "missing data reconstruction in remote sensing image with a unified spatial temporal spectral deep convolutional neural network"
    - "multi temporal land cover classification with sequential recurrent encoders"
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "understanding deep learning requires rethinking generalization"

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)