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: "a survey on object detection in optical remote sensing images"
  Abstract: Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

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 "era a dataset and deep learning benchmark for event recognition in aerial videos"):
  with members:
    - "object detection in optical remote sensing images a survey and a new benchmark"
    - "drone based joint density map estimation localization and tracking with space time multi scale attention network"
    - "half a percent of labels is enough efficient animal detection in uav imagery using deep cnns and active learning"
    - "deep learning based aerial image classification for emergency response applications using unmanned aerial vehicles"
    - "a survey of deep learning based object detection"
    - "r 3 net a deep network for multioriented vehicle detection in aerial images and videos"
    - "mrcnet crowd counting and density map estimation in aerial and ground imagery"
    - "detecting mammals in uav images best practices to address a substantially imbalanced dataset with deep learning"

Connected through hyperedge HE-3 (induced by source paper "cad net a context aware detection network for objects in remote sensing imagery"):
  with members:
    - "locality and structure regularized low rank representation for hyperspectral image classification"
    - "position detection and direction prediction for arbitrary oriented ships via multitask rotation region convolutional neural network"
    - "automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks"
    - "towards multi class object detection in unconstrained remote sensing imagery"
    - "attention driven person re identification"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "dota a large scale dataset for object detection in aerial images"
    - "deformable convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "r 3 net a deep network for multioriented vehicle detection in aerial images and videos"):
  with members:
    - "yolov3 an incremental improvement"
    - "vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network"
    - "automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks"
    - "rifcn recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images"
    - "dota a large scale dataset for object detection in aerial images"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "r 2 cnn fast tiny object detection in large scale remote sensing images"):
  with members:
    - "s 3 fd single shot scale invariant face detector"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "ssd single shot multibox detector"
    - "training region based object detectors with online hard example mining"
    - "feature pyramid networks for object detection"
    - "pyramid scene parsing network"
    - "finding tiny faces"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "satellite imagery multiscale rapid detection with windowed networks"):
  with members:
    - "xview objects in context in overhead imagery"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "road extraction by deep residual u net"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-7 (induced by source paper "online structured sparsity based moving object detection from satellite videos"):
  with members:
    - "error bounded foreground and background modeling for moving object detection in satellite videos"
    - "dota a large scale dataset for object detection in aerial images"
    - "tracking in aerial hyperspectral videos using deep kernelized correlation filters"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "corola a sequential solution to moving object detection using low rank approximation"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"

Connected through hyperedge HE-8 (induced by source paper "nested network with two stream pyramid for salient object detection in optical remote sensing images"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "review of visual saliency detection with comprehensive information"
    - "co saliency detection for rgbd images based on multi constraint feature matching and cross label propagation"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "an iterative co saliency framework for rgbd images"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "structure measure a new way to evaluate foreground maps"
    - "remote sensing image scene classification benchmark and state of the art"

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)