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: "spectral spatial feature extraction and classification by ann supervised with center loss in hyperspectral imagery"
  Abstract: In this paper, we propose a spectral-spatial feature extraction and classification framework based on artificial neuron network (ANN) in the context of hyperspectral imagery. With limited labeled samples, only spectral information is exploited for training and spatial context is integrated posteriorly at the testing stage. Taking advantage of recent advances in face recognition, a joint supervision symbol that combines softmax loss and center loss is adopted to train the proposed network, by which intra-class features are gathered while inter-class variations are enlarged. Based on the learned architecture, the extracted spectrum-based features are classified by a center classifier. Moreover, to fuse the spectral and spatial information, an adaptive spectral-spatial center classifier is developed, where multiscale neighborhoods are considered simultaneously, and the final label is determined using an adaptive voting strategy. Finally, experimental results on three well-known datasets validate the effectiveness of the proposed methods compared with the state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "a cnn based spatial feature fusion algorithm for hyperspectral imagery classification"):
  with members:
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "bidirectional convolutional lstm based spectral spatial feature learning for hyperspectral image classification"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "tensorflow a system for large scale machine learning"
    - "deep learning in neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-2 (induced by source paper "hyperspectral image classification with deep metric learning and conditional random field"):
  with members:
    - "a cnn based spatial feature fusion algorithm for hyperspectral imagery classification"
    - "convolutional crfs for semantic segmentation"
    - "superpca a superpixelwise pca approach for unsupervised feature extraction of hyperspectral imagery"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "bidirectional convolutional lstm based spectral spatial feature learning for hyperspectral image classification"
    - "caffe convolutional architecture for fast feature embedding"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

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)