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: "spatial aware dictionary learning for hyperspectral image classification"
  Abstract: This paper presents a structured dictionary-based model for hyperspectral data that incorporates both spectral and contextual characteristics of a spectral sample, with the goal of hyperspectral image classification. The idea is to partition the pixels of a hyperspectral image into a number of spatial neighborhoods called contextual groups and to model each pixel with a linear combination of a few dictionary elements learned from the data. Since pixels inside a contextual group are often made up of the same materials, their linear combinations are constrained to use common elements from the dictionary. To this end, dictionary learning is carried out with a joint sparse regularizer to induce a common sparsity pattern in the sparse coefficients of each contextual group. The sparse coefficients are then used for classification using a linear SVM. Experimental results on a number of real hyperspectral images confirm the effectiveness of the proposed representation for hyperspectral image classification. Moreover, experiments with simulated multispectral data show that the proposed model is capable of finding representations that may effectively be used for classification of multispectral-resolution samples.

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"
    - "structured priors for sparse representation based 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 "multiscale principle of relevant information for hyperspectral image classification"):
  with members:
    - "multivariate extension of matrix based renyi s alpha order entropy functional"
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-3 (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"
    - "structured priors for sparse representation based hyperspectral image classification"

Connected through hyperedge HE-4 (induced by source paper "shapelet based sparse representation for landcover classification of hyperspectral images"):
  with members:
    - "structured priors for sparse representation based hyperspectral image classification"
    - "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"
    - "incremental import vector machines for classifying hyperspectral data"
    - "rank awareness in joint sparse recovery"

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)