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: "deep learning in remote sensing a review"
  Abstract: Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.

Connected through hyperedge HE-1 (induced by source paper "dynamic multicontext segmentation of remote sensing images based on convolutional networks"):
  with members:
    - "exploiting convnet diversity for flooding identification"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"
    - "a survey of semantic segmentation"
    - "classification with an edge improving semantic image segmentation with boundary detection"

Connected through hyperedge HE-2 (induced by source paper "map net multi attending path neural network for building footprint extraction from remote sensed imagery"):
  with members:
    - "esfnet efficient network for building extraction from high resolution aerial images"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "ocnet object context network for scene parsing"
    - "automatic pixelwise object labeling for aerial imagery using stacked u nets"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "dual attention network for scene segmentation"

Connected through hyperedge HE-3 (induced by source paper "semantic referee a neural symbolic framework for enhancing geospatial semantic segmentation"):
  with members:
    - "location augmentation for cnn"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "comprehensive survey of deep learning in remote sensing theories tools and challenges for the community"
    - "explaining trained neural networks with semantic web technologies first steps"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "relating input concepts to convolutional neural network decisions"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "how a general purpose commonsense ontology can improve performance of learning based image retrieval"

Connected through hyperedge HE-4 (induced by source paper "a framework for remote sensing images processing using deep learning techniques"):
  with members:
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "a survey on object detection in optical remote sensing images"
    - "a generic framework for the development of geospatial processing pipelines on clusters"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "target adaptive cnn based pansharpening"):
  with members:
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"
    - "boosting the accuracy of multispectral image pansharpening by learning a deep residual network"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a survey of pansharpening methods with a new band decoupled variational model"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"

Connected through hyperedge HE-6 (induced by source paper "what goes where predicting object distributions from above"):
  with members:
    - "understanding and mapping natural beauty"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "predicting ground level scene layout from aerial imagery"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "imagenet large scale visual recognition challenge"

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)