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 "shapelet based sparse representation for landcover classification of hyperspectral images"):
  Abstract: This paper presents a sparse-representation-based classification approach with a novel dictionary construction procedure. By using the constructed dictionary, sophisticated prior knowledge about the spatial nature of the image can be integrated. The approach is based on the assumption that each image patch can be factorized into characteristic spatial patterns, also called shapelets, and patch-specific spectral information. A set of shapelets is learned in an unsupervised way, and spectral information is embodied by training samples. A combination of shapelets and spectral information is represented in an undercomplete spatial–spectral dictionary for each individual patch, where the elements of the dictionary are linearly combined to a sparse representation of the patch. The patch-based classification is obtained by means of the representation error. Experiments are conducted on three well-known hyperspectral image data sets. They illustrate that our proposed approach shows superior results in comparison to sparse-representation-based classifiers that use only limited spatial information and behaves competitively with or better than state-of-the-art classifiers utilizing spatial information and kernelized sparse-representation-based classifiers.

Includes members:
  Member "structured priors for sparse representation based hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "locality and structure regularized low rank representation for hyperspectral image classification")
  Member "advances in hyperspectral image classification earth monitoring with statistical learning methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d quasi recurrent neural network for hyperspectral image denoising")
  Member "new algorithms for learning incoherent and overcomplete dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "when can wasserstein gans minimize wasserstein distance")
  Member "efficient high dimensional inference in the multiple measurement vector problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "spatial aware dictionary learning for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locality and structure regularized low rank representation for hyperspectral image classification")
  Member "incremental import vector machines for classifying hyperspectral data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tropical land use land cover mapping in para brazil using discriminative markov random fields and multi temporal terrasar x data")
  Member "rank awareness in joint sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")

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)