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: "feature pyramid and hierarchical boosting network for pavement crack detection"
  Abstract: Pavement crack detection is a critical task for insuring road safety. Manual crack detection is extremely time-consuming. Therefore, an automatic road crack detection method is required to boost this progress. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavements and possible shadows with similar intensity. Inspired by recent advances of deep learning in computer vision, we propose a novel network architecture, named Feature Pyramid and Hierarchical Boosting Network (FPHBN), for pavement crack detection. The proposed network integrates semantic information to low-level features for crack detection in a feature pyramid way. And, it balances the contribution of both easy and hard samples to loss by nested sample reweighting in a hierarchical way. To demonstrate the superiority and generality of the proposed method, we evaluate the proposed method on five crack datasets and compare it with state-of-the-art crack detection, edge detection, semantic segmentation methods. Extensive experiments show that the proposed method outperforms these state-of-the-art methods in terms of accuracy and generality.

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:
    - "automatic pavement crack detection based on structured prediction with the convolutional neural network"
    - "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 "unsupervised pixel level road defect detection via adversarial image to frequency transform"):
  with members:
    - "road crack detection using deep convolutional neural network and adaptive thresholding"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "richer convolutional features for edge detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "future frame prediction for anomaly detection a new baseline"
    - "holistically nested edge detection"
    - "adam a method for stochastic optimization"

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:
    - "automatic pavement crack detection based on structured prediction with the convolutional neural network"
    - "road damage detection using deep neural networks with images captured through a smartphone"

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"
    - "yolov3 an incremental improvement"
    - "road damage detection using deep neural networks with images captured through a smartphone"
    - "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"

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)