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: "dual local global contextual pathways for recognition in aerial imagery"
  Abstract: Visual context is important in object recognition and it is still an open problem in computer vision. Along with the advent of deep convolutional neural networks (CNN), using contextual information with such systems starts to receive attention in the literature. At the same time, aerial imagery is gaining momentum. While advances in deep learning make good progress in aerial image analysis, this problem still poses many great challenges. Aerial images are often taken under poor lighting conditions and contain low resolution objects, many times occluded by trees or taller buildings. In this domain, in particular, visual context could be of great help, but there are still very few papers that consider context in aerial image understanding. Here we introduce context as a complementary way of recognizing objects. We propose a dual-stream deep neural network model that processes information along two independent pathways, one for local and another for global visual reasoning. The two are later combined in the final layers of processing. Our model learns to combine local object appearance as well as information from the larger scene at the same time and in a complementary way, such that together they form a powerful classifier. We test our dual-stream network on the task of segmentation of buildings and roads in aerial images and obtain state-of-the-art results on the Massachusetts Buildings Dataset. We also introduce two new datasets, for buildings and road segmentation, respectively, and study the relative importance of local appearance vs. the larger scene, as well as their performance in combination. While our local-global model could also be useful in general recognition tasks, we clearly demonstrate the effectiveness of visual context in conjunction with deep nets for aerial image understanding.

Connected through hyperedge HE-1 (induced by source paper "dynamic multicontext segmentation of remote sensing images based on convolutional networks"):
  with members:
    - "exploiting convnet diversity for flooding identification"
    - "deep learning in remote sensing a review"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"
    - "a survey of semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep learning in remote sensing a review"):
  with members:
    - "toward seamless multiview scene analysis from satellite to street level"
    - "look wider to match image patches with convolutional neural networks"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "lift learned invariant feature transform"
    - "a survey on object detection in optical remote sensing images"
    - "gvnn neural network library for geometric computer vision"

Connected through hyperedge HE-3 (induced by source paper "classification with an edge improving semantic image segmentation with boundary detection"):
  with members:
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "learning to refine object segments"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"
    - "holistically nested edge detection"

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)