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 "feature extraction for hyperspectral imagery the evolution from shallow to deep"):
  Abstract: Hyperspectral images provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands) with continuous spectral information that can accurately classify diverse materials of interest. The increased dimensionality of such data makes it possible to significantly improve data information content but provides a challenge to the conventional techniques (the so-called curse of dimensionality) for accurate analysis of hyperspectral images. Feature extraction, as a vibrant field of research in the hyperspectral community, evolved through decades of research to address this issue and extract informative features suitable for data representation and classification. The advances in feature extraction have been inspired by two fields of research, including the popularization of image and signal processing as well as machine (deep) learning, leading to two types of feature extraction approaches named shallow and deep techniques. This article outlines the advances in feature extraction approaches for hyperspectral imagery by providing a technical overview of the state-of-the-art techniques, providing useful entry points for researchers at different levels, including students, researchers, and senior researchers, willing to explore novel investigations on this challenging topic. % by supplying a rich amount of detail and references. In more detail, this paper provides a bird's eye view over shallow (both supervised and unsupervised) and deep feature extraction approaches specifically dedicated to the topic of hyperspectral feature extraction and its application on hyperspectral image classification. Additionally, this paper compares 15 advanced techniques with an emphasis on their methodological foundations in terms of classification accuracies.

Includes members:
  Member "simultaneous spectral spatial feature selection and extraction for hyperspectral images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spectral pyramid graph attention network for hyperspectral image classification")
  Member "orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency channel features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "invariant attribute profiles a spatial frequency joint feature extractor for hyperspectral image classification")
  Member "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a framework for large scale mapping of human settlement extent from sentinel 2 images via fully convolutional neural networks")
  Member "cospace common subspace learning from hyperspectral multispectral correspondences":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a framework for large scale mapping of human settlement extent from sentinel 2 images via fully convolutional neural networks")
  Member "an augmented linear mixing model to address spectral variability for hyperspectral unmixing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a framework for large scale mapping of human settlement extent from sentinel 2 images via fully convolutional neural networks")
  Member "active transfer learning network a unified deep joint spectral spatial feature learning model for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting embodiedqa a simple baseline and beyond")
  Member "joint progressive learning from high dimensional data for multi label classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "invariant attribute profiles a spatial frequency joint feature extractor for hyperspectral image classification")
  Member "cascaded recurrent neural networks for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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.CV(Computer Vision and Pattern Recognition)