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: "land use classification in remote sensing images by convolutional neural networks"
  Abstract: We explore the use of convolutional neural networks for the semantic classification of remote sensing scenes. Two recently proposed architectures, CaffeNet and GoogLeNet, are adopted, with three different learning modalities. Besides conventional training from scratch, we resort to pre-trained networks that are only fine-tuned on the target data, so as to avoid overfitting problems and reduce design time. Experiments on two remote sensing datasets, with markedly different characteristics, testify on the effectiveness and wide applicability of the proposed solution, which guarantees a significant performance improvement over all state-of-the-art references.

Connected through hyperedge HE-1 (induced by source paper "temporal interpolation of geostationary satellite imagery with task specific optical flow"):
  with members:
    - "depth aware video frame interpolation"
    - "super resolution of sentinel 2 images learning a globally applicable deep neural network"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "video frame interpolation via adaptive separable convolution"
    - "video frame synthesis using deep voxel flow"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "deepsat a learning framework for satellite imagery"

Connected through hyperedge HE-2 (induced by source paper "deep discriminative representation learning with attention map for scene classification"):
  with members:
    - "on the convergence of adam and beyond"
    - "weakly supervised instance segmentation using class peak response"
    - "bam bottleneck attention module"
    - "deep attentional structured representation learning for visual recognition"
    - "learn to pay attention"
    - "cbam convolutional block attention module"
    - "attention is all you need"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-3 (induced by source paper "in domain representation learning for remote sensing"):
  with members:
    - "the visual task adaptation benchmark"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "domain adaptive transfer learning with specialist models"
    - "exploring the limits of weakly supervised pretraining"
    - "marta gans unsupervised representation learning for remote sensing image classification"
    - "comprehensive survey of deep learning in remote sensing theories tools and challenges for the community"
    - "filmy cloud removal on satellite imagery with multispectral conditional generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "understanding urban landuse from the above and ground perspectives a deep learning multimodal solution"):
  with members:
    - "deep multi task learning for a geographically regularized semantic segmentation of aerial images"
    - "fine grained land use classification at the city scale using ground level images"
    - "building instance classification using street view images"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "large scale land cover classification in gaofen 2 satellite imagery"
    - "toward seamless multiview scene analysis from satellite to street level"
    - "a unified model for near and remote sensing"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "adversarial example in remote sensing image recognition"):
  with members:
    - "deepdetect a cascaded region based densely connected network for seismic event detection"
    - "detnet a backbone network for object detection"
    - "adversarial attacks and defences a survey"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "remote sensing image scene classification benchmark and state of the art"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-6 (induced by source paper "human machine collaboration for fast land cover mapping"):
  with members:
    - "dense fusion classmate network for land cover classification"
    - "group normalization"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "urban land cover classification with missing data modalities using deep convolutional neural networks"
    - "prototypical networks for few shot learning"
    - "machine teaching a new paradigm for building machine learning systems"
    - "film visual reasoning with a general conditioning layer"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-7 (induced by source paper "optical flow for intermediate frame interpolation of multispectral geostationary satellite data"):
  with members:
    - "super resolution of sentinel 2 images learning a globally applicable deep neural network"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "video frame interpolation via adaptive convolution"
    - "video frame interpolation via adaptive separable convolution"
    - "deepsd generating high resolution climate change projections through single image super resolution"
    - "spatio temporal video autoencoder with differentiable memory"
    - "u net convolutional networks for biomedical image segmentation"
    - "deepsat a learning framework for satellite imagery"

Connected through hyperedge HE-8 (induced by source paper "deep learning emulation of multi angle implementation of atmospheric correction maiac"):
  with members:
    - "quantifying uncertainty in discrete continuous and skewed data with bayesian deep learning"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deepsd generating high resolution climate change projections through single image super resolution"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "deepsat a learning framework for satellite imagery"
    - "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)