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: "structured priors for sparse representation based hyperspectral image classification"
  Abstract: Pixelwise classification, where each pixel is assigned to a predefined class, is one of the most important procedures in hyperspectral image (HSI) analysis. By representing a test pixel as a linear combination of a small subset of labeled pixels, a sparse representation classifier (SRC) gives rather plausible results compared with that of traditional classifiers such as the support vector machine. Recently, by incorporating additional structured sparsity priors, the second-generation SRCs have appeared in the literature and are reported to further improve the performance of HSI. These priors are based on exploiting the spatial dependences between the neighboring pixels, the inherent structure of the dictionary, or both. In this letter, we review and compare several structured priors for sparse-representation-based HSI classification. We also propose a new structured prior called the low-rank (LR) group prior, which can be considered as a modification of the LR prior. Furthermore, we will investigate how different structured priors improve the result for the HSI classification.

Connected through hyperedge HE-1 (induced by source paper "locality and structure regularized low rank representation for hyperspectral image classification"):
  with members:
    - "getnet a general end to end 2 d cnn framework for hyperspectral image change detection"
    - "spatial aware dictionary learning for hyperspectral image classification"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "moving object detection by detecting contiguous outliers in the low rank representation"

Connected through hyperedge HE-2 (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"
    - "spatial aware dictionary learning for hyperspectral image classification"

Connected through hyperedge HE-3 (induced by source paper "fast low rank shared dictionary learning for image classification"):
  with members:
    - "learning a low rank shared dictionary for object classification"
    - "adaptive matching pursuit for sparse signal recovery"
    - "dfdl discriminative feature oriented dictionary learning for histopathological image classification"
    - "task driven dictionary learning for hyperspectral image classification with structured sparsity constraints"
    - "multi task image classification via collaborative hierarchical spike and slab priors"
    - "collaborative multi sensor classification via sparsity based representation"

Connected through hyperedge HE-4 (induced by source paper "shapelet based sparse representation for landcover classification of hyperspectral images"):
  with members:
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "spatial aware dictionary learning for hyperspectral image classification"
    - "incremental import vector machines for classifying hyperspectral data"
    - "rank awareness in joint sparse recovery"

Connected through hyperedge HE-5 (induced by source paper "task driven dictionary learning for hyperspectral image classification with structured sparsity constraints"):
  with members:
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"
    - "robust recovery of subspace structures by low rank representation"
    - "a tutorial on spectral clustering"

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)