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: "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
  Abstract: Road detection from the perspective of moving vehicles is a challenging issue in autonomous driving. Recently, many deep learning methods spring up for this task, because they can extract high-level local features to find road regions from raw RGB data, such as convolutional neural networks and fully convolutional networks (FCNs). However, how to detect the boundary of road accurately is still an intractable problem. In this paper, we propose siamesed FCNs (named “s-FCN-loc”), which is able to consider RGB-channel images, semantic contours, and location priors simultaneously to segment the road region elaborately. To be specific, the s-FCN-loc has two streams to process the original RGB images and contour maps, respectively. At the same time, the location prior is directly appended to the siamesed FCN to promote the final detection performance. Our contributions are threefold: 1) An s-FCN-loc is proposed that learns more discriminative features of road boundaries than the original FCN to detect more accurate road regions. 2) Location prior is viewed as a type of feature map and directly appended to the final feature map in s-FCN-loc to promote the detection performance effectively, which is easier than other traditional methods, namely, different priors for different inputs (image patches). 3) The convergent speed of training s-FCN-loc model is 30% faster than the original FCN because of the guidance of highly structured contours. The proposed approach is evaluated on the KITTI road detection benchmark and one-class road detection data set, and achieves a competitive result with the state of the arts.

Connected through hyperedge HE-1 (induced by source paper "sinet a scale insensitive convolutional neural network for fast vehicle detection"):
  with members:
    - "a joint convolutional neural networks and context transfer for street scenes labeling"
    - "3d bounding box estimation using deep learning and geometry"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "aggregated residual transformations for deep neural networks"
    - "ssd single shot multibox detector"
    - "training region based object detectors with online hard example mining"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised adversarial domain adaptation for semantic segmentation in urban scenes"):
  with members:
    - "adversarial discriminative domain adaptation"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "dsod learning deeply supervised object detectors from scratch"
    - "exploiting saliency for object segmentation from image level labels"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep reconstruction classification networks for unsupervised domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "joint activity recognition and indoor localization with wifi fingerprints"):
  with members:
    - "continuous user authentication by contactless wireless sensing"
    - "csi net unified human body characterization and action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "position tracking for virtual reality using commodity wifi"
    - "tensorbeat tensor decomposition for monitoring multi person breathing beats with commodity wifi"
    - "mask r cnn"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"

Connected through hyperedge HE-4 (induced by source paper "deep representation learning for road detection through siamese network"):
  with members:
    - "dynamically visual disambiguation of keyword based image search"
    - "extracting visual knowledge from the internet making sense of image data"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "fast lidar based road detection using fully convolutional neural networks"
    - "exploiting web images for dataset construction a domain robust approach"
    - "mask r cnn"
    - "multinet real time joint semantic reasoning for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "deep neural network and data augmentation methodology for off axis iris segmentation in wearable headsets"):
  with members:
    - "segdensenet iris segmentation for pre and post cataract surgery"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "a survey of augmented reality navigation"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "gesture based continuous authentication for wearable devices the google glass case"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "mapping road lanes using laser remission and deep neural networks"):
  with members:
    - "a model predictive motion planner for the iara autonomous car"
    - "automatic large scale data acquisition via crowdsourcing for crosswalk classification a deep learning approach"
    - "fast lidar based road detection using fully convolutional neural networks"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "lidar based driving path generation using fully convolutional neural networks"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-7 (induced by source paper "missing data reconstruction in remote sensing image with a unified spatial temporal spectral deep convolutional neural network"):
  with members:
    - "a joint convolutional neural networks and context transfer for street scenes labeling"
    - "boosting the accuracy of multispectral image pansharpening by learning a deep residual network"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "context encoders feature learning by inpainting"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "learning a dilated residual network for sar image despeckling"):
  with members:
    - "sar image despeckling through convolutional neural networks"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "enhanced deep residual networks for single image super resolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "a neural approach to blind motion deblurring"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"

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)