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: "unsupervised deep feature extraction for remote sensing image classification"
  Abstract: This paper introduces the use of   single-layer   and   deep   convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of   greedy layerwise unsupervised pretraining   coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on   sparse representations   and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features   only   when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy.

Connected through hyperedge HE-1 (induced by source paper "active reward learning for co robotic vision based exploration in bandwidth limited environments"):
  with members:
    - "information guided robotic maximum seek and sample in partially observable continuous environments"
    - "streaming scene maps for co robotic exploration in bandwidth limited environments"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "an approach to autonomous science by modeling geological knowledge in a bayesian framework"
    - "feature discovery and visualization of robot mission data using convolutional autoencoders and bayesian nonparametric topic models"
    - "modeling curiosity in a mobile robot for long term autonomous exploration and monitoring"
    - "anomaly detection in unstructured environments using bayesian nonparametric scene modeling"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-2 (induced by source paper "improving the detection of burnt areas in remote sensing using hyper features evolved by m3gp"):
  with members:
    - "ensemble genetic programming"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "convolutional sparse kernel network for unsupervised medical image analysis"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "a survey on deep learning in medical image analysis"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "all you need is a good init"

Connected through hyperedge HE-5 (induced by source paper "deep clustering with intra class distance constraint for hyperspectral images"):
  with members:
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
    - "trace quotient with sparsity priors for learning low dimensional image representations"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deep subspace clustering networks"
    - "a multimodal anomaly detector for robot assisted feeding using an lstm based variational autoencoder"
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "unsupervised deep embedding for clustering analysis"
    - "convolutional clustering for unsupervised learning"

Connected through hyperedge HE-6 (induced by source paper "a scalable saliency based feature selection method with instance level information"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "why should i trust you explaining the predictions of any classifier"
    - "wide residual networks"
    - "adversarial machine learning at scale"
    - "tensorflow a system for large scale machine learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "understanding deep image representations by inverting them"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "geomorphological analysis using unpiloted aircraft systems structure from motion and deep learning"):
  with members:
    - "monocular camera based fruit counting and mapping with semantic data association"
    - "robust fruit counting combining deep learning tracking and structure from motion"
    - "mask r cnn"
    - "feature pyramid networks for object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"

Connected through hyperedge HE-8 (induced by source paper "a cnn based spatial feature fusion algorithm for hyperspectral imagery classification"):
  with members:
    - "spectral spatial feature extraction and classification by ann supervised with center loss in hyperspectral imagery"
    - "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"
    - "tensorflow a system for large scale machine learning"
    - "deep learning in neural networks"
    - "caffe convolutional architecture for fast feature embedding"

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)