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: "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.

Connected through hyperedge HE-1 (induced by source paper "going deeper with contextual cnn for hyperspectral image classification"):
  with members:
    - "active deep learning for classification of hyperspectral images"
    - "contextual deep cnn based hyperspectral classification"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "contextual deep cnn based hyperspectral classification"):
  with members:
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for semantic segmentation"

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)