human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep learning for classification of hyperspectral data a comparative review"):
  Abstract: In recent years, deep-learning techniques revolutionized the way remote sensing data are processed. The classification of hyperspectral data is no exception to the rule, but it has intrinsic specificities that make the application of deep learning less straightforward than with other optical data. This article presents the state of the art of previous machine-learning approaches, reviews the various deeplearning approaches currently proposed for hyperspectral classification, and identifies the problems and difficulties that arise in the implementation of deep neural networks for this task. In particular, the issues of spatial and spectral resolution, data volume, and transfer of models from multimedia images to hyperspectral data are addressed. Additionally, a comparative study of various families of network architectures is provided, and a software toolbox is publicly released to allow experimenting with these methods (https://github.com/nshaud/DeepHyperX). This article is intended for both data scientists with interest in hyperspectral data and remote sensing experts eager to apply deeplearning techniques to their own data set.

Includes members:
  Member "low shot learning for the semantic segmentation of remote sensing imagery":
    also connected through other hyperedges:
      (none)
  Member "gmm based synthetic samples for classification of hyperspectral images with limited training data":
    also connected through other hyperedges:
      (none)
  Member "3 d deep learning approach for remote sensing image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning hyperspectral feature extraction and classification with resnext network")
  Member "hsi cnn a novel convolution neural network for hyperspectral image":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolution based spectral partitioning architecture for hyperspectral image classification")
  Member "generative adversarial networks for realistic synthesis of hyperspectral samples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hyperspectral data augmentation")
  Member "going deeper with contextual cnn for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep manifold embedding for hyperspectral image classification")
  Member "endnet sparse autoencoder network for endmember extraction and hyperspectral unmixing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improved deep spectral convolution network for hyperspectral unmixing with multinomial mixture kernel and endmember uncertainty")
  Member "cascaded recurrent neural networks for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bs nets an end to end framework for band selection of hyperspectral image")

Question: Please tell me which class the source paper 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.LG(Machine Learning)