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: "going deeper with contextual cnn for hyperspectral image classification"
  Abstract: In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike current state-of-the-art approaches in CNN-based hyperspectral image classification, the proposed network, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors. The joint exploitation of the spatio-spectral information is achieved by a multi-scale convolutional filter bank used as an initial component of the proposed CNN pipeline. The initial spatial and spectral feature maps obtained from the multi-scale filter bank are then combined together to form a joint spatio-spectral feature map. The joint feature map representing rich spectral and spatial properties of the hyperspectral image is then fed through a fully convolutional network that eventually predicts the corresponding label of each pixel vector. The proposed approach is tested on three benchmark data sets: the Indian Pines data set, the Salinas data set, and the University of Pavia data set. Performance comparison shows enhanced classification performance of the proposed approach over the current state-of-the-art on the three data sets.

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"
    - "gmm based synthetic samples for classification of hyperspectral images with limited training data"
    - "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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "deep manifold embedding for hyperspectral image classification"):
  with members:
    - "diversity in machine learning"
    - "geometric understanding of deep learning"
    - "rethinking feature distribution for loss functions in image classification"
    - "mining on manifolds metric learning without labels"
    - "image reconstruction by domain transform manifold learning"
    - "deep class wise hashing semantics preserving hashing via class wise loss"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep metric learning with angular loss"

Connected through hyperedge HE-3 (induced by source paper "effective transfer learning for hyperspectral image classification with deep convolutional neural networks"):
  with members:
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "gate variants of gated recurrent unit gru neural networks"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "how transferable are features in deep neural networks"
    - "network in network"

Connected through hyperedge HE-4 (induced by source paper "an introduction to deep morphological networks"):
  with members:
    - "dynamic multicontext segmentation of remote sensing images based on convolutional networks"
    - "an interactive medical image segmentation framework using iterative refinement"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "xception deep learning with depthwise separable convolutions"
    - "visual saliency detection based on multiscale deep cnn features"
    - "deep residual learning for image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "statistical loss and analysis for deep learning in hyperspectral image classification"):
  with members:
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "deep metric learning with angular loss"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep metric learning via lifted structured feature embedding"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "is pretraining necessary for hyperspectral image classification"):
  with members:
    - "rethinking imagenet pre training"
    - "exploring the limits of weakly supervised pretraining"
    - "cross domain cnn for hyperspectral image classification"
    - "dod cnn doubly injecting object information for event recognition"
    - "iod cnn integrating object detection networks for event recognition"
    - "contextual deep cnn based hyperspectral classification"

Connected through hyperedge HE-7 (induced by source paper "three dimensional fourier scattering transform and classification of hyperspectral images"):
  with members:
    - "simultaneous spectral spatial feature selection and extraction for hyperspectral images"
    - "spectral spatial classification of hyperspectral images three tricks and a new learning setting"
    - "dynamic routing between capsules"
    - "deep scattering spectrum"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-8 (induced by source paper "validating hyperspectral image segmentation"):
  with members:
    - "bass net band adaptive spectral spatial feature learning neural network for hyperspectral image classification"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

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)