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: "aerial image geolocalization from recognition and matching of roads and intersections"
  Abstract: Aerial image analysis at a semantic level is important in many applications with strong potential impact in industry and consumer use, such as automated mapping, urban planning, real estate and environment monitoring, or disaster relief. The problem is enjoying a great interest in computer vision and remote sensing, due to increased computer power and improvement in automated image understanding algorithms. In this paper we address the task of automatic geolocalization of aerial images from recognition and matching of roads and intersections. Our proposed method is a novel contribution in the literature that could enable many applications of aerial image analysis when GPS data is not available. We offer a complete pipeline for geolocalization, from the detection of roads and intersections, to the identification of the enclosing geographic region by matching detected intersections to previously learned manually labeled ones, followed by accurate geometric alignment between the detected roads and the manually labeled maps. We test on a novel dataset with aerial images of two European cities and use the publicly available OpenStreetMap project for collecting ground truth roads annotations. We show in extensive experiments that our approach produces highly accurate localizations in the challenging case when we train on images from one city and test on the other and the quality of the aerial images is relatively poor. We also show that the the alignment between detected roads and pre-stored manual annotations can be effectively used for improving the quality of the road detection results.

Connected through hyperedge HE-1 (induced by source paper "multi temporal aerial image registration using semantic features"):
  with members:
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "deep residual learning for image recognition"
    - "learning to compare image patches via convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "cross domain image matching with deep feature maps"):
  with members:
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "image to image translation with conditional adversarial networks"
    - "computing the stereo matching cost with a convolutional neural network"
    - "learning to compare image patches via convolutional neural networks"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-3 (induced by source paper "inferring and improving street maps with data driven automation"):
  with members:
    - "assigning a grade accurate measurement of road quality using satellite imagery"
    - "machine assisted map editing"
    - "roadtracer automatic extraction of road networks from aerial images"
    - "a comparison and evaluation of map construction algorithms using vehicle tracking data"

Connected through hyperedge HE-4 (induced by source paper "feature based groupwise registration of historical aerial images to present day ortho photo maps"):
  with members:
    - "image stitching by line guided local warping with global similarity constraint"
    - "mods fast and robust method for two view matching"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"

Connected through hyperedge HE-5 (induced by source paper "road network based rapid geolocalization"):
  with members:
    - "google map aided visual navigation for uavs in gps denied environment"

Connected through hyperedge HE-6 (induced by source paper "a multi stage multi task neural network for aerial scene interpretation and geolocalization"):
  with members:
    - "automated map reading image based localisation in 2 d maps using binary semantic descriptors"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "multi task learning for segmentation of building footprints with deep neural networks"
    - "an overview of multi task learning in deep neural networks"
    - "pixels to graphs by associative embedding"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "a review on deep learning techniques applied to semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "roadtracer automatic extraction of road networks from aerial images"):
  with members:
    - "kharita robust map inference using graph spanners"
    - "a comparison and evaluation of map construction algorithms using vehicle tracking data"

Connected through hyperedge HE-8 (induced by source paper "joint learning from earth observation and openstreetmap data to get faster better semantic maps"):
  with members:
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "automatic building extraction in aerial scenes using convolutional networks"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "image to image translation with conditional adversarial networks"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"
    - "classification with an edge improving semantic image segmentation with boundary detection"
    - "deep residual learning for 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)