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: "automatic discovery and geotagging of objects from street view imagery"
  Abstract: Many applications such as autonomous navigation, urban planning and asset monitoring, rely on the availability of accurate information about objects and their geolocations. In this paper we propose to automatically detect and compute the GPS coordinates of recurring stationary objects of interest using street view imagery. Our processing pipeline relies on two fully convolutional neural networks: the first segments objects in the images while the second estimates their distance from the camera. To geolocate all the detected objects coherently we propose a novel custom Markov Random Field model to perform objects triangulation. The novelty of the resulting pipeline is the combined use of monocular depth estimation and triangulation to enable automatic mapping of complex scenes with multiple visually similar objects of interest. We validate experimentally the effectiveness of our approach on two object classes: traffic lights and telegraph poles. The experiments report high object recall rates and GPS accuracy within 2 meters, which is comparable with the precision of single-frequency GPS receivers.

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"
    - "toward seamless multiview scene analysis from satellite to street level"
    - "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"
    - "toward seamless multiview scene analysis from satellite to street level"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

Connected through hyperedge HE-3 (induced by source paper "automated detecting and placing road objects from street level images"):
  with members:
    - "yolov3 an incremental improvement"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "learning to navigate in cities without a map"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep learning the city quantifying urban perception at a global scale"
    - "pyramid scene parsing network"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-4 (induced by source paper "automatic detection of passable roads after floods in remote sensed and social media data"):
  with members:
    - "exploiting convnet diversity for flooding identification"
    - "retinal vessel segmentation in fundoscopic images with generative adversarial networks"
    - "enriching word vectors with subword information"
    - "yfcc100m the new data in multimedia research"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "rethinking the inception architecture for computer vision"

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)