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: "automatic pavement crack detection based on structured prediction with the convolutional neural network"
  Abstract: Automated pavement crack detection is a challenging task that has been researched for decades due to the complicated pavement conditions in real world. In this paper, a supervised method based on deep learning is proposed, which has the capability of dealing with different pavement conditions. Specifically, a convolutional neural network (CNN) is used to learn the structure of the cracks from raw images, without any preprocessing. Small patches are extracted from crack images as inputs to generate a large training database, a CNN is trained and crack detection is modeled as a multi-label classification problem. Typically, crack pixels are much fewer than non-crack pixels. To deal with the problem with severely imbalanced data, a strategy with modifying the ratio of positive to negative samples is proposed. The method is tested on two public databases and compared with five existing methods. Experimental results show that it outperforms the other methods.

Connected through hyperedge HE-1 (induced by source paper "automated pavement crack segmentation using fully convolutional u net with a pretrained resnet 34 encoder"):
  with members:
    - "feature pyramid and hierarchical boosting network for pavement crack detection"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "decoupled weight decay regularization"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "ensemble of deep convolutional neural networks for automatic pavement crack detection and measurement"):
  with members:
    - "ensemble of deep convolutional neural networks for learning to detect retinal vessels in fundus images"
    - "u net convolutional networks for biomedical image segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-3 (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"
    - "road damage detection using deep neural networks with images captured through a smartphone"

Connected through hyperedge HE-4 (induced by source paper "image based reconstruction for the impact problems by using dpnns"):
  with members:
    - "deep learning for determining a near optimal topological design without any iteration"
    - "exploring the 3d architectures of deep material network in data driven multiscale mechanics"
    - "3d topology optimization using convolutional neural networks"
    - "image based reconstruction for a 3d pfhs heat transfer problem by reconnn"
    - "how to control the crack to propagate along the specified path feasibly"
    - "neural networks for topology optimization"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "fpcnet fast pavement crack detection network based on encoder decoder architecture"):
  with members:
    - "squeeze and excitation networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "anonet weakly supervised anomaly detection in textured surfaces"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "understanding neural networks via feature visualization a survey"
    - "ablation of a robot s brain neural networks under a knife"
    - "rethinking atrous convolution for semantic image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-7 (induced by source paper "image based reconstruction for strong nonlinear transient problems by using an enhanced reconnn"):
  with members:
    - "image based reconstruction for a 3d pfhs heat transfer problem by reconnn"
    - "how to control the crack to propagate along the specified path feasibly"
    - "neural networks for topology optimization"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "pid a new benchmark dataset to classify and densify pavement distresses"):
  with members:
    - "road damage detection using deep neural networks with images captured through a smartphone"
    - "u net convolutional networks for biomedical image segmentation"

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)