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: "advances in hyperspectral image classification earth monitoring with statistical learning methods"
  Abstract: Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. The framework of statistical learning has gained popularity in the last decade. New methods have been presented to account for the spatial homogeneity of images, to include user's interaction via active learning, to take advantage of the manifold structure with semisupervised learning, to extract and encode invariances, or to adapt classifiers and image representations to unseen yet similar scenes. This tutuorial reviews the main advances for hyperspectral remote sensing image classification through illustrative examples.

Connected through hyperedge HE-1 (induced by source paper "3d quasi recurrent neural network for hyperspectral image denoising"):
  with members:
    - "residual non local attention networks for image restoration"
    - "hyperspectral image denoising employing a spatial spectral deep residual convolutional neural network"
    - "residual dense network for image restoration"
    - "non local meets global an integrated paradigm for hyperspectral denoising"
    - "weighted low rank tensor recovery for hyperspectral image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "denoising hyperspectral image with non i i d noise structure"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-2 (induced by source paper "feature extraction and classification based on spatial spectral convlstm neural network for hyperspectral images"):
  with members:
    - "script identification in natural scene image and video frames using an attention based convolutional lstm network"
    - "dynamic routing between capsules"
    - "bidirectional convolutional lstm based spectral spatial feature learning for hyperspectral image classification"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "training very deep networks"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (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"
    - "bass net band adaptive spectral spatial feature learning neural network for hyperspectral image classification"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "hybridsn exploring 3d 2d cnn feature hierarchy for hyperspectral image classification"):
  with members:
    - "lisht non parametric linearly scaled hyperbolic tangent activation function for neural networks"
    - "3 d deep learning approach for remote sensing image classification"
    - "dual cnn models for unsupervised monocular depth estimation"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "mask r cnn"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "online sensor hallucination via knowledge distillation for multimodal image classification"):
  with members:
    - "urban land cover classification with missing data modalities using deep convolutional neural networks"
    - "modality distillation with multiple stream networks for action recognition"
    - "graph distillation for action detection with privileged modalities"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "cross modal distillation for supervision transfer"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-6 (induced by source paper "1d convolutional capsule network for hyperspectral image classification"):
  with members:
    - "fast capsnet for lung cancer screening"
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "3 d deep learning approach for remote sensing image classification"
    - "spectral spatial classification of hyperspectral images three tricks and a new learning setting"
    - "dynamic routing between capsules"
    - "a convolutional neural network for modelling sentences"

Connected through hyperedge HE-7 (induced by source paper "multitask deep learning with spectral knowledge for hyperspectral image classification"):
  with members:
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "cascaded recurrent neural networks for hyperspectral image classification"

Connected through hyperedge HE-8 (induced by source paper "simultaneous spectral spatial feature selection and extraction for hyperspectral images"):
  with members:
    - "multi view intact space learning"
    - "representation learning a review and new perspectives"

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)