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: "simultaneous spectral spatial feature selection and extraction for hyperspectral images"
  Abstract: In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.

Connected through hyperedge HE-1 (induced by source paper "feature extraction for hyperspectral imagery the evolution from shallow to deep"):
  with members:
    - "orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency channel features"
    - "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification"
    - "cospace common subspace learning from hyperspectral multispectral correspondences"
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
    - "active transfer learning network a unified deep joint spectral spatial feature learning model for hyperspectral image classification"
    - "joint progressive learning from high dimensional data for multi label classification"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "bidirectional convolutional lstm based spectral spatial feature learning for hyperspectral image classification"

Connected through hyperedge HE-2 (induced by source paper "spectral pyramid graph attention network for hyperspectral image classification"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "hyperspectral image classification with context aware dynamic graph convolutional network"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "adaptive graph convolutional neural networks"
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "large scale learnable graph convolutional networks"
    - "non local neural networks"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-4 (induced by source paper "three dimensional fourier scattering transform and classification of hyperspectral images"):
  with members:
    - "spectral spatial classification of hyperspectral images three tricks and a new learning setting"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "dynamic routing between capsules"
    - "deep scattering spectrum"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-5 (induced by source paper "superpca a superpixelwise pca approach for unsupervised feature extraction of hyperspectral imagery"):
  with members:
    - "one two one networks for compression artifacts reduction in remote sensing"
    - "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"

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)