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: "xview objects in context in overhead imagery"
  Abstract: We introduce a new large-scale dataset for the advancement of object detection techniques and overhead object detection research. This satellite imagery dataset enables research progress pertaining to four key computer vision frontiers. We utilize a novel process for geospatial category detection and bounding box annotation with three stages of quality control. Our data is collected from WorldView-3 satellites at 0.3m ground sample distance, providing higher resolution imagery than most public satellite imagery datasets. We compare xView to other object detection datasets in both natural and overhead imagery domains and then provide a baseline analysis using the Single Shot MultiBox Detector. xView is one of the largest and most diverse publicly available object-detection datasets to date, with over 1 million objects across 60 classes in over 1,400 km^2 of imagery.

Connected through hyperedge HE-1 (induced by source paper "generating interpretable poverty maps using object detection in satellite images"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "efficient object detection in large images using deep reinforcement learning"
    - "yolov3 an incremental improvement"
    - "consistent individualized feature attribution for tree ensembles"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-2 (induced by source paper "discoverability in satellite imagery a good sentence is worth a thousand pictures"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "exploring models and data for remote sensing image caption generation"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "where to put the image in an image caption generator"
    - "functional map of the world"
    - "remote sensing image scene classification benchmark and state of the art"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "efficient object detection in large images using deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "reduced focal loss 1st place solution to xview object detection in satellite imagery"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "using pre training can improve model robustness and uncertainty"
    - "learning to interpret satellite images using wikipedia"
    - "autofocus efficient multi scale inference"
    - "yolov3 an incremental improvement"
    - "enkcf ensemble of kernelized correlation filters for high speed object tracking"

Connected through hyperedge HE-4 (induced by source paper "deep built structure counting in satellite imagery using attention based re weighting"):
  with members:
    - "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"
    - "cross view image matching for geo localization in urban environments"

Connected through hyperedge HE-5 (induced by source paper "conditional generative adversarial networks for data augmentation and adaptation in remotely sensed imagery"):
  with members:
    - "multimodal 3d object detection from simulated pretraining"
    - "domain adaptive generation of aircraft on satellite imagery via simulated and unsupervised learning"
    - "scargan chained generative adversarial networks to simulate pathological tissue on cardiovascular mr scans"
    - "pioneer networks progressively growing generative autoencoder"
    - "deep learning in medical image analysis and multimodal learning for clinical decision support 4th international workshop dlmia 2018 and 8th international workshop ml cds 2018 held in conjunction with miccai 2018 granada spain september 20 2018 proceedings"
    - "a style based generator architecture for generative adversarial networks"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"

Connected through hyperedge HE-6 (induced by source paper "spacenet mvoi a multi view overhead imagery dataset"):
  with members:
    - "spacenet a remote sensing dataset and challenge series"
    - "spatially transformed adversarial examples"
    - "an occluded stacked hourglass approach to facial landmark localization and occlusion estimation"
    - "the effects of super resolution on object detection performance in satellite imagery"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-7 (induced by source paper "a comparison of super resolution and nearest neighbors interpolation applied to object detection on satellite data"):
  with members:
    - "a new loss function for cnn classifier based on pre defined evenly distributed class centroids"
    - "the effects of super resolution on object detection performance in satellite imagery"
    - "cofga classification of fine grained features in aerial images"
    - "deep back projection networks for super resolution"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "focal loss for dense object detection"
    - "dota a large scale dataset for object detection in aerial images"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-8 (induced by source paper "satellite imagery multiscale rapid detection with windowed networks"):
  with members:
    - "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"
    - "a survey on object detection in optical remote sensing images"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"

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)