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: "toward seamless multiview scene analysis from satellite to street level"
  Abstract: In this paper, we discuss and review how combined multiview imagery from satellite to street level can benefit scene analysis. Numerous works exist that merge information from remote sensing and images acquired from the ground for tasks such as object detection, robots guidance, or scene understanding. What makes the combination of overhead and street-level images challenging are the strongly varying viewpoints, the different scales of the images, their illuminations and sensor modality, and time of acquisition. Direct (dense) matching of images on a per-pixel basis is thus often impossible, and one has to resort to alternative strategies that will be discussed in this paper. For such purpose, we review recent works that attempt to combine images taken from the ground and overhead views for purposes like scene registration, reconstruction, or classification. After the theoretical review, we present three recent methods to showcase the interest and potential impact of such fusion on real applications (change detection, image orientation, and tree cataloging), whose logic can then be reused to extend the use of ground-based images in remote sensing and  vice versa . Through this review, we advocate that cross fertilization between remote sensing, computer vision, and machine learning is very valuable to make the best of geographic data available from Earth observation sensors and ground imagery. Despite its challenges, we believe that integrating these complementary data sources will lead to major breakthroughs in Big GeoData. It will open new perspectives for this exciting and emerging field.

Connected through hyperedge HE-1 (induced by source paper "geocoding of trees from street addresses and street level images"):
  with members:
    - "simultaneous multi view instance detection with learned geometric soft constraints"
    - "from google maps to a fine grained catalog of street trees"
    - "automatic discovery and geotagging of objects from street view imagery"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "predicting ground level scene layout from aerial imagery"
    - "multi view 3d object detection network for autonomous driving"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "simultaneous multi view instance detection with learned geometric soft constraints"):
  with members:
    - "learning pose specific representations by predicting different views"
    - "rpnet an end to end network for relative camera pose estimation"
    - "from google maps to a fine grained catalog of street trees"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "accurate deep direct geo localization from ground imagery and phone grade gps"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "automatic discovery and geotagging of objects from street view imagery"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

Connected through hyperedge HE-3 (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"
    - "a unified model for near and remote sensing"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"

Connected through hyperedge HE-4 (induced by source paper "image to gps verification through a bottom up pattern matching network"):
  with members:
    - "oatm occlusion aware template matching by consensus set maximization"
    - "selective deep convolutional features for image retrieval"
    - "deep matching and validation network an end to end solution to constrained image splicing localization and detection"
    - "revisiting im2gps in the deep learning era"
    - "planet photo geolocation with convolutional neural networks"
    - "large scale image retrieval with attentive deep local features"
    - "face recognition using deep multi pose representations"
    - "template matching with deformable diversity similarity"

Connected through hyperedge HE-5 (induced by source paper "multisource and multitemporal data fusion in remote sensing"):
  with members:
    - "effective sequential classifier training for svm based multitemporal remote sensing image classification"
    - "comprehensive survey of deep learning in remote sensing theories tools and challenges for the community"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "very deep convolutional networks for large scale image recognition"
    - "hyperspectral and multispectral image fusion based on a sparse representation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "change detection between multimodal remote sensing data using siamese cnn"):
  with members:
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"
    - "computing the stereo matching cost with a convolutional neural network"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "deep learning in remote sensing a review"):
  with members:
    - "look wider to match image patches with convolutional neural networks"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "lift learned invariant feature transform"
    - "a survey on object detection in optical remote sensing images"
    - "gvnn neural network library for geometric computer vision"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"

Connected through hyperedge HE-8 (induced by source paper "automatic discovery and geotagging of objects from street view imagery"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "unsupervised monocular depth estimation with left right consistency"
    - "ssd single shot multibox detector"
    - "deeper depth prediction with fully convolutional residual networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

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)