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 "deepseed 3d squeeze and excitation encoder decoder convnets for pulmonary nodule detection"):
  Abstract: Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans. Although promising performance has been achieved by deep learning based nodule detection methods, it remains challenging to build nodule detection networks with good generalization performance due to unbalanced positive and negative samples. In order to overcome this problem and further improve state-of-the-art region proposal network methods, we develop a novel deep 3D convolutional neural network with an Encoder-Decoder structure for pulmonary nodule detection. Particularly, we utilize a dynamically scaled cross entropy loss to reduce the false positive rate and compensate the significant data imbalance problem. We adopt the squeeze-and-excitation structure to learn effective image features and fully utilize channel inter-dependency. We have validated our method based on publicly available CT scans from LIDC/IDRI dataset and its subset LUNA16 with thinner slices. Ablation studies and experimental results have demonstrated that our method could outperform state-of-the-art nodule detection methods by a large margin, with an average FROC score of 86.2% on LUNA16, and an average FROC score of 77.3% on LIDC/IDRI dataset when trained on LUNA16 only.

Includes members:
  Member "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d aggregated faster r cnn for general lesion detection")
  Member "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "s4nd single shot single scale lung nodule detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a 3d probabilistic deep learning system for detection and diagnosis of lung cancer using low dose ct scans")
  Member "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "no surprises training robust lung nodule detection for low dose ct scans by augmenting with adversarial attacks")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning for automated medical image analysis")
  Member "squeeze and excitation networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "no surprises training robust lung nodule detection for low dose ct scans by augmenting with adversarial attacks")

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)