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: "you only look twice rapid multi scale object detection in satellite imagery"
  Abstract: Detection of small objects in large swaths of imagery is one of the primary problems in satellite imagery analytics. While object detection in ground-based imagery has benefited from research into new deep learning approaches, transitioning such technology to overhead imagery is nontrivial. Among the challenges is the sheer number of pixels and geographic extent per image: a single DigitalGlobe satellite image encompasses >64 km2 and over 250 million pixels. Another challenge is that objects of interest are minuscule (often only ~10 pixels in extent), which complicates traditional computer vision techniques. To address these issues, we propose a pipeline (You Only Look Twice, or YOLT) that evaluates satellite images of arbitrary size at a rate of >0.5 km2/s. The proposed approach can rapidly detect objects of vastly different scales with relatively little training data over multiple sensors. We evaluate large test images at native resolution, and yield scores of F1 > 0.8 for vehicle localization. We further explore resolution and object size requirements by systematically testing the pipeline at decreasing resolution, and conclude that objects only ~5 pixels in size can still be localized with high confidence. Code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "deep active learning for remote sensing object detection"):
  with members:
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "yolov3 an incremental improvement"
    - "cost effective object detection active sample mining with switchable selection criteria"
    - "towards human machine cooperation self supervised sample mining for object detection"
    - "robust face detection via learning small faces on hard images"
    - "learning roi transformer for detecting oriented objects in aerial images"
    - "active learning for cost sensitive classification"

Connected through hyperedge HE-2 (induced by source paper "spacenet mvoi a multi view overhead imagery dataset"):
  with members:
    - "spacenet a remote sensing dataset and challenge series"
    - "xview objects in context in overhead imagery"
    - "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"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-3 (induced by source paper "satellite imagery multiscale rapid detection with windowed networks"):
  with members:
    - "xview objects in context in overhead 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"

Connected through hyperedge HE-4 (induced by source paper "training set effect on super resolution for automated target recognition"):
  with members:
    - "on training deep networks for satellite image super resolution"
    - "deep learning for single image super resolution a brief review"
    - "srgan training dataset matters"
    - "a deep journey into super resolution a survey"
    - "yolov3 an incremental improvement"
    - "the effects of super resolution on object detection performance in satellite imagery"
    - "mask r cnn"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "seeing isn t believing practical adversarial attack against object detectors"):
  with members:
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "adversarial patch"
    - "feature denoising for improving adversarial robustness"
    - "deep learning for generic object detection a survey"
    - "yolov3 an incremental improvement"
    - "realistic adversarial examples in 3d meshes"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "the effects of super resolution on object detection performance in satellite imagery"):
  with members:
    - "satellite imagery multiscale rapid detection with windowed networks"
    - "spacenet a remote sensing dataset and challenge series"
    - "xview objects in context in overhead imagery"
    - "fast and accurate single image super resolution via information distillation network"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "task driven super resolution object detection in low resolution images"
    - "residual dense network for image super resolution"
    - "deep laplacian pyramid networks for fast and accurate super resolution"

Connected through hyperedge HE-7 (induced by source paper "spacenet a remote sensing dataset and challenge series"):
  with members:
    - "xview objects in context in overhead imagery"
    - "functional map of the world"
    - "a multi task convolutional neural network for mega city analysis using very high resolution satellite imagery and geospatial data"
    - "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "yolo9000 better faster stronger"
    - "torontocity seeing the world with a million eyes"
    - "pyramid scene parsing network"

Connected through hyperedge HE-8 (induced by source paper "guiding the creation of deep learning based object detectors"):
  with members:
    - "yolov3 an incremental improvement"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "ssd single shot multibox detector"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "how transferable are features in deep neural networks"
    - "imagenet large scale visual recognition challenge"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)