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: "gmm based synthetic samples for classification of hyperspectral images with limited training data"
  Abstract: The amount of training data that is required to train a classifier scales with the dimensionality of the feature data. In hyperspectral remote sensing (HSRS), feature data can potentially become very high dimensional. However, the amount of training data is oftentimes limited. Thus, one of the core challenges in HSRS is how to perform multiclass classification using only relatively few training data points. In this letter, we address this issue by enriching the feature matrix with synthetically generated sample points. These synthetic data are sampled from a Gaussian mixture model (GMM) fitted to each class of the limited training data. Although the true distribution of features may not be perfectly modeled by the fitted GMM, we demonstrate that a moderate augmentation by these synthetic samples can effectively replace a part of the missing training samples. Doing so, the median gain in classification performance is 5% on two datasets. This performance gain is stable for variations in the number of added samples, which makes it easy to apply this method to real-world applications.

Connected through hyperedge HE-1 (induced by source paper "deep learning for classification of hyperspectral data a comparative review"):
  with members:
    - "low shot learning for the semantic segmentation of remote sensing imagery"
    - "3 d deep learning approach for remote sensing image classification"
    - "hsi cnn a novel convolution neural network for hyperspectral image"
    - "generative adversarial networks for realistic synthesis of hyperspectral samples"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "endnet sparse autoencoder network for endmember extraction and hyperspectral unmixing"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"

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)