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: "hyperspectral cnn classification with limited training samples"
  Abstract: Hyperspectral imaging sensors are becoming increasingly popular in robotics applications such as agriculture and mining, and allow per-pixel thematic classification of materials in a scene based on their unique spectral signatures. Recently, convolutional neural networks have shown remarkable performance for classification tasks, but require substantial amounts of labelled training data. This data must sufficiently cover the variability expected to be encountered in the environment. For hyperspectral data, one of the main variations encountered outdoors is due to incident illumination, which can change in spectral shape and intensity depending on the scene geometry. For example, regions occluded from the sun have a lower intensity and their incident irradiance skewed towards shorter wavelengths. #R##N#In this work, a data augmentation strategy based on relighting is used during training of a hyperspectral convolutional neural network. It allows training to occur in the outdoor environment given only a small labelled region, which does not need to sufficiently represent the geometric variability of the entire scene. This is important for applications where obtaining large amounts of training data is labourious, hazardous or difficult, such as labelling pixels within shadows. Radiometric normalisation approaches for pre-processing the hyperspectral data are analysed and it is shown that methods based on the raw pixel data are sufficient to be used as input for the classifier. This removes the need for external hardware such as calibration boards, which can restrict the application of hyperspectral sensors in robotics applications. Experiments to evaluate the classification system are carried out on two datasets captured from a field-based platform.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks for realistic synthesis of hyperspectral samples"):
  with members:
    - "improved training of wasserstein gans"
    - "inverting variational autoencoders for improved generative accuracy"
    - "improved techniques for training gans"
    - "contextual deep cnn based hyperspectral classification"

Connected through hyperedge HE-2 (induced by source paper "sound source localization in a multipath environment using convolutional neural networks"):
  with members:
    - "broadband doa estimation using convolutional neural networks trained with noise signals"
    - "convolutional neural networks for passive monitoring of a shallow water environment using a single sensor"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)