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: "road damage detection using deep neural networks with images captured through a smartphone"
  Abstract: Research on damage detection of road surfaces using image processing techniques has been actively conducted, achieving considerably high detection accuracies. Many studies only focus on the detection of the presence or absence of damage. However, in a real-world scenario, when the road managers from a governing body need to repair such damage, they need to clearly understand the type of damage in order to take effective action. In addition, in many of these previous studies, the researchers acquire their own data using different methods. Hence, there is no uniform road damage dataset available openly, leading to the absence of a benchmark for road damage detection. This study makes three contributions to address these issues. First, to the best of our knowledge, for the first time, a large-scale road damage dataset is prepared. This dataset is composed of 9,053 road damage images captured with a smartphone installed on a car, with 15,435 instances of road surface damage included in these road images. In order to generate this dataset, we cooperated with 7 municipalities in Japan and acquired road images for more than 40 hours. These images were captured in a wide variety of weather and illuminance conditions. In each image, we annotated the bounding box representing the location and type of damage. Next, we used a state-of-the-art object detection method using convolutional neural networks to train the damage detection model with our dataset, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method. The road damage dataset, our experimental results, and the developed smartphone application used in this study are publicly available (this https URL).

Connected through hyperedge HE-1 (induced by source paper "a deep neural networks approach for pixel level runway pavement crack segmentation using drone captured images"):
  with members:
    - "feature pyramid and hierarchical boosting network for pavement crack detection"
    - "automatic pavement crack detection based on structured prediction with the convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "driving datasets literature review"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "blvd building a large scale 5d semantics benchmark for autonomous driving"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "object detection in 20 years a survey"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "the streetlearn environment and dataset"

Connected through hyperedge HE-3 (induced by source paper "adversarial adaptation of scene graph models for understanding civic issues"):
  with members:
    - "generating triples with adversarial networks for scene graph construction"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "multi adversarial domain adaptation"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "neural motifs scene graph parsing with global context"
    - "adversarial discriminative domain adaptation"
    - "scene graph generation by iterative message passing"
    - "scene graph generation from objects phrases and region captions"

Connected through hyperedge HE-4 (induced by source paper "a cost effective solution for road crack inspection using cameras and deep neural networks"):
  with members:
    - "connnet a long range relation aware pixel connectivity network for salient segmentation"
    - "feature pyramid and hierarchical boosting network for pavement crack detection"
    - "yolov3 an incremental improvement"
    - "image to image translation with conditional adversarial networks"
    - "conditional generative adversarial nets"
    - "fully convolutional networks for semantic segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "pid a new benchmark dataset to classify and densify pavement distresses"):
  with members:
    - "automatic pavement crack detection based on structured prediction with the convolutional neural network"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "road damage detection acquisition system based on deep neural networks for physical asset management"):
  with members:
    - "deep learning for generic object detection a survey"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-7 (induced by source paper "applying domain randomization to synthetic data for object category detection"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning from simulated and unsupervised images through adversarial training"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"

Connected through hyperedge HE-8 (induced by source paper "localizing and quantifying damage in social media images"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "social media data analysis and feedback for advanced disaster risk management"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "deep residual learning for image recognition"
    - "learning deep features for discriminative localization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "processing social media messages in mass emergency a survey"

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)