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: "cross view image matching for geo localization in urban environments"
  Abstract: In this paper, we address the problem of cross-view image geo-localization. Specifically, we aim to estimate the GPS location of a query street view image by finding the matching images in a reference database of geo-tagged bird's eye view images, or vice versa. To this end, we present a new framework for cross-view image geo-localization by taking advantage of the tremendous success of deep convolutional neural networks (CNNs) in image classification and object detection. First, we employ the Faster R-CNN to detect buildings in the query and reference images. Next, for each building in the query image, we retrieve the $k$ nearest neighbors from the reference buildings using a Siamese network trained on both positive matching image pairs and negative pairs. To find the correct NN for each query building, we develop an efficient multiple nearest neighbors matching method based on dominant sets. We evaluate the proposed framework on a new dataset that consists of pairs of street view and bird's eye view images. Experimental results show that the proposed method achieves better geo-localization accuracy than other approaches and is able to generalize to images at unseen locations.

Connected through hyperedge HE-1 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "gabor convolutional networks"
    - "recent advances in object detection in the age of deep convolutional neural networks"
    - "deep learning for generic object detection a survey"
    - "yolov3 an incremental improvement"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "deep adaptive proposal network for object detection in optical remote sensing images"

Connected through hyperedge HE-2 (induced by source paper "university 1652 a multi view multi source benchmark for drone based geo localization"):
  with members:
    - "building information modeling and classification by visual learning at a city scale"
    - "triplet based deep hashing network for cross modal retrieval"
    - "lending orientation to neural networks for cross view geo localization"
    - "optimal feature transport for cross view image geo localization"
    - "vision meets drones a challenge"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "revisiting oxford and paris large scale image retrieval benchmarking"

Connected through hyperedge HE-3 (induced by source paper "evaluation of cross view matching to improve ground vehicle localization with aerial perception"):
  with members:
    - "leveraging photogrammetric mesh models for aerial ground feature point matching toward integrated 3d reconstruction"
    - "bridging the domain gap for ground to aerial image matching"
    - "lending orientation to neural networks for cross view geo localization"
    - "project autovision localization and 3d scene perception for an autonomous vehicle with a multi camera system"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "semantic cross view matching"
    - "wide area image geolocalization with aerial reference imagery"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "bridging the domain gap for ground to aerial image matching"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "from third person to first person dataset and baselines for synthesis and retrieval"
    - "cross view image synthesis using conditional gans"
    - "what is it like down there generating dense ground level views and image features from overhead imagery using conditional generative adversarial networks"
    - "cross view image synthesis using geometry guided conditional gans"
    - "generative adversarial frontal view to bird view synthesis"
    - "in defense of the triplet loss for person re identification"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "cross view policy learning for street navigation"):
  with members:
    - "learning to follow directions in street view"
    - "the streetlearn environment and dataset"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "global pose estimation with an attention based recurrent network"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "the adobeindoornav dataset towards deep reinforcement learning based real world indoor robot visual navigation"
    - "unsupervised predictive memory in a goal directed agent"

Connected through hyperedge HE-6 (induced by source paper "deep built structure counting in satellite imagery using attention based re weighting"):
  with members:
    - "xview objects in context in overhead imagery"
    - "exploring models and data for remote sensing image caption generation"
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "detecting and counting tiny faces"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

Connected through hyperedge HE-7 (induced by source paper "image based geo localization using satellite imagery"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "satellite image based localization via learned embeddings"
    - "deep metric learning with angular loss"
    - "revisiting im2gps in the deep learning era"
    - "localizing and orienting street views using overhead imagery"
    - "densely connected convolutional networks"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-8 (induced by source paper "lending orientation to neural networks for cross view geo localization"):
  with members:
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep stochastic attraction and repulsion embedding for image based localization"
    - "cross view image synthesis using conditional gans"
    - "deep complex networks"
    - "deep metric learning with angular loss"
    - "on complex valued convolutional neural networks"
    - "localizing and orienting street views using overhead imagery"
    - "densely connected convolutional networks"

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)