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: "bass net band adaptive spectral spatial feature learning neural network for hyperspectral image classification"
  Abstract: Deep learning based land cover classification algorithms have recently been proposed in the literature. In hyperspectral images (HSIs), they face the challenges of large dimensionality, spatial variability of spectral signatures, and scarcity of labeled data. In this paper, we propose an end-to-end deep learning architecture that extracts band specific spectral-spatial features and performs land cover classification. The architecture has fewer independent connection weights and thus requires fewer training samples. The method is found to outperform the highest reported accuracies on popular HSI data sets.

Connected through hyperedge HE-1 (induced by source paper "a cnn with multi scale convolution for hyperspectral image classification using target pixel orientation scheme"):
  with members:
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "deep learning hyperspectral image classification using multiple class based denoising autoencoders mixed pixel training augmentation and morphological operations"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "punch positive unlabelled classification based information retrieval in hyperspectral images"):
  with members:
    - "ladder networks for semi supervised hyperspectral image classification"
    - "positive unlabeled learning with non negative risk estimator"
    - "unsupervised classification in hyperspectral imagery with nonlocal total variation and primal dual hybrid gradient algorithm"
    - "generative adversarial positive unlabelled learning"
    - "learning to match using local and distributed representations of text for web search"
    - "deep learning in neural networks"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"

Connected through hyperedge HE-3 (induced by source paper "transfer learning for segmenting dimensionally reduced hyperspectral images"):
  with members:
    - "validating hyperspectral image segmentation"
    - "is pretraining necessary for hyperspectral image classification"
    - "active transfer learning network a unified deep joint spectral spatial feature learning model for hyperspectral image classification"
    - "band selection from hyperspectral images using attention based convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "validating hyperspectral image segmentation"):
  with members:
    - "going deeper with contextual cnn 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"

Connected through hyperedge HE-5 (induced by source paper "segmenting hyperspectral images using spectral spatial convolutional neural networks with training time data augmentation"):
  with members:
    - "validating hyperspectral image segmentation"
    - "unsupervised segmentation of hyperspectral images using 3d convolutional autoencoders"
    - "3 d deep learning approach for remote sensing image classification"

Connected through hyperedge HE-6 (induced by source paper "hyperspectral data augmentation"):
  with members:
    - "validating hyperspectral image segmentation"
    - "automatic brain tumor segmentation using convolutional neural networks with test time augmentation"
    - "generative adversarial networks for realistic synthesis of hyperspectral samples"

Connected through hyperedge HE-7 (induced by source paper "unsupervised segmentation of hyperspectral images using 3d convolutional autoencoders"):
  with members:
    - "transfer learning for segmenting dimensionally reduced hyperspectral images"
    - "validating hyperspectral image segmentation"

Connected through hyperedge HE-8 (induced by source paper "convolution based spectral partitioning architecture for hyperspectral image classification"):
  with members:
    - "hsi cnn a novel convolution neural network for hyperspectral image"

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)