human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "automated pavement crack segmentation using fully convolutional u net with a pretrained resnet 34 encoder"):
  Abstract: Automated pavement crack segmentation is a challenging task because of inherent irregular patterns and lighting conditions, in addition to the presence of noise in images. Conventional approaches require a substantial amount of feature engineering to differentiate crack regions from non-affected regions. In this paper, we propose a deep learning technique based on a convolutional neural network to perform segmentation tasks on pavement crack images. Our approach requires minimal feature engineering compared to other machine learning techniques. The proposed neural network architecture is a modified U-Net in which the encoder is replaced with a pretrained ResNet-34 network. To minimize the dice coefficient loss function, we optimize the parameters in the neural network by using an adaptive moment optimizer called AdamW. Additionally, we use a systematic method to find the optimum learning rate instead of doing parametric sweeps. We used a "one-cycle" training schedule based on cyclical learning rates to speed up the convergence. We evaluated the performance of our convolutional neural network on CFD, a pavement crack image dataset. Our method achieved an F1 score of about 96%. This is the best performance among all other algorithms tested on this dataset, outperforming the previous best method by a 1.7% margin.

Includes members:
  Member "feature pyramid and hierarchical boosting network for pavement crack detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised pixel level road defect detection via adversarial image to frequency transform")
  Member "automatic pavement crack detection based on structured prediction with the convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ensemble of deep convolutional neural networks for automatic pavement crack detection and measurement")
  Member "concurrent spatial and channel squeeze excitation in fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery")
  Member "decoupled weight decay regularization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "v net fully convolutional neural networks for volumetric medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "cyclical learning rates for training neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)