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: "spectral spatial classification of hyperspectral image using autoencoders"
  Abstract: Hyperspectral image (HSI) classification is a hot topic in the remote sensing community. This paper proposes a new framework of spectral-spatial feature extraction for HSI classification, in which for the first time the concept of deep learning is introduced. Specifically, the model of autoencoder is exploited in our framework to extract various kinds of features. First we verify the eligibility of autoencoder by following classical spectral information based classification and use autoencoders with different depth to classify hyperspectral image. Further in the proposed framework, we combine PCA on spectral dimension and autoencoder on the other two spatial dimensions to extract spectral-spatial information for classification. The experimental results show that this framework achieves the highest classification accuracy among all methods, and outperforms classical classifiers such as SVM and PCA-based SVM.

Connected through hyperedge HE-1 (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"
    - "3 d deep learning approach for remote sensing image classification"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"):
  with members:
    - "remote sensing image scene classification benchmark and state of the art"
    - "aggregated residual transformations for deep neural networks"
    - "identity mappings in deep residual networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "synthesizing training images for boosting human 3d pose estimation"
    - "spectral angle based unary energy functions for spatial spectral hyperspectral classification using markov random fields"
    - "learning to segment object candidates"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "hyperspectral image classification with markov random fields and a convolutional neural network"):
  with members:
    - "clearing the skies a deep network architecture for single image rain removal"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "unsupervised classification in hyperspectral imagery with nonlocal total variation and primal dual hybrid gradient algorithm"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "image super resolution using deep convolutional networks"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "structured priors for sparse representation based hyperspectral image classification"

Connected through hyperedge HE-4 (induced by source paper "low shot learning for the semantic segmentation of remote sensing imagery"):
  with members:
    - "improved techniques for training gans"
    - "parametric exponential linear unit for deep convolutional neural networks"
    - "semi supervised learning with ladder networks"
    - "fast and accurate deep network learning by exponential linear units elus"

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)