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: "aid a benchmark data set for performance evaluation of aerial scene classification"
  Abstract: Aerial scene classification, which aims to automatically label an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery. In recent years, it has become an active task in the remote sensing area, and numerous algorithms have been proposed for this task, including many machine learning and data-driven approaches. However, the existing data sets for aerial scene classification, such as UC-Merced data set and WHU-RS19, contain relatively small sizes, and the results on them are already saturated. This largely limits the development of scene classification algorithms. This paper describes the Aerial Image data set (AID): a large-scale data set for aerial scene classification. The goal of AID is to advance the state of the arts in scene classification of remote sensing images. For creating AID, we collect and annotate more than 10000 aerial scene images. In addition, a comprehensive review of the existing aerial scene classification techniques as well as recent widely used deep learning methods is given. Finally, we provide a performance analysis of typical aerial scene classification and deep learning approaches on AID, which can be served as the baseline results on this benchmark.

Connected through hyperedge HE-1 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis"):
  with members:
    - "foal fast online adaptive learning for cardiac motion estimation"
    - "higherhrnet scale aware representation learning for bottom up human pose estimation"
    - "differential treatment for stuff and things a simple unsupervised domain adaptation method for semantic segmentation"
    - "convolutional networks with dense connectivity"
    - "bottom up higher resolution networks for multi person pose estimation"
    - "isaid a large scale dataset for instance segmentation in aerial images"
    - "any precision deep neural networks"
    - "skynet a hardware efficient method for object detection and tracking on embedded systems"

Connected through hyperedge HE-2 (induced by source paper "convolution neural network architecture learning for remote sensing scene classification"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"
    - "remote sensing image scene classification benchmark and state of the art"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-3 (induced by source paper "bigearthnet dataset with a new class nomenclature for remote sensing image understanding"):
  with members:
    - "a novel multi attention driven system for multi label remote sensing image classification"
    - "bigearthnet a large scale benchmark archive for remote sensing image understanding"
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "building instance classification using street view images"
    - "towards multi class object detection in unconstrained remote sensing imagery"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "remote sensing image scene classification benchmark and state of the art"

Connected through hyperedge HE-4 (induced by source paper "relation network for multi label aerial image classification"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "understanding urban landuse from the above and ground perspectives a deep learning multimodal solution"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "recent advances and opportunities in scene classification of aerial images with deep models"
    - "vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network"
    - "rifcn recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images"
    - "fine grained object recognition and zero shot learning in remote sensing imagery"
    - "im2height height estimation from single monocular imagery via fully residual convolutional deconvolutional network"

Connected through hyperedge HE-5 (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-6 (induced by source paper "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"):
  with members:
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network"
    - "rifcn recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images"
    - "im2height height estimation from single monocular imagery via fully residual convolutional deconvolutional network"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "remote sensing image scene classification benchmark and state of the art"

Connected through hyperedge HE-7 (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"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "mask r cnn"
    - "a survey on object detection in optical remote sensing images"

Connected through hyperedge HE-8 (induced by source paper "deep discriminative representation learning with attention map for scene classification"):
  with members:
    - "on the convergence of adam and beyond"
    - "weakly supervised instance segmentation using class peak response"
    - "bam bottleneck attention module"
    - "deep attentional structured representation learning for visual recognition"
    - "learn to pay attention"
    - "cbam convolutional block attention module"
    - "attention is all you need"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

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)