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: "vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network"
  Abstract: Object detection and semantic segmentation are two main themes in object retrieval from high-resolution remote sensing images, which have recently achieved remarkable performance by surfing the wave of deep learning and, more notably, convolutional neural networks. In this paper, we are interested in a novel, more challenging problem of vehicle instance segmentation, which entails identifying, at a pixel level, where the vehicles appear as well as associating each pixel with a physical instance of a vehicle. In contrast, vehicle detection and semantic segmentation each only concern one of the two. We propose to tackle this problem with a semantic boundary-aware multitask learning network. More specifically, we utilize the philosophy of residual learning to construct a fully convolutional network that is capable of harnessing multilevel contextual feature representations learned from different residual blocks. We theoretically analyze and discuss why residual networks can produce better probability maps for pixelwise segmentation tasks. Then, based on this network architecture, we propose a unified multitask learning network that can simultaneously learn two complementary tasks, namely, segmenting vehicle regions and detecting semantic boundaries. The latter subproblem is helpful for differentiating “touching” vehicles that are usually not correctly separated into instances. Currently, data sets with a pixelwise annotation for vehicle extraction are the ISPRS data set and the IEEE GRSS DFC2015 data set over Zeebrugge, which specializes in a semantic segmentation. Therefore, we built a new, more challenging data set for vehicle instance segmentation, called the  Busy Parking Lot Unmanned Aerial Vehicle Video data set , and we make our data set available at  http://www.sipeo.bgu.tum.de/downloads  so that it can be used to benchmark future vehicle instance segmentation algorithms.

Connected through hyperedge HE-1 (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"
    - "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"
    - "relation networks for object detection"

Connected through hyperedge HE-2 (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"
    - "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"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "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-3 (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"
    - "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"
    - "a survey on object detection in optical remote sensing images"

Connected through hyperedge HE-4 (induced by source paper "ai based pilgrim detection using convolutional neural networks"):
  with members:
    - "activity monitoring of islamic prayer salat postures using deep learning"
    - "aerial images processing for car detection using convolutional neural networks comparison between faster r cnn and yolov3"
    - "colormapgan unsupervised domain adaptation for semantic segmentation using color mapping generative adversarial networks"
    - "unsupervised domain adaptation using generative adversarial networks for semantic segmentation of aerial images"
    - "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks"
    - "yolov3 an incremental improvement"
    - "car detection using unmanned aerial vehicles comparison between faster r cnn and yolov3"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-5 (induced by source paper "contextual hourglass network for semantic segmentation of high resolution aerial imagery"):
  with members:
    - "dual attention network for scene segmentation"
    - "learning aerial image segmentation from online maps"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "adam a method for stochastic optimization"

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)