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 "nodulenet decoupled false positive reductionfor pulmonary nodule detection and segmentation"):
  Abstract: Pulmonary nodule detection, false positive reduction and segmentation represent three of the most common tasks in the computeraided analysis of chest CT images. Methods have been proposed for eachtask with deep learning based methods heavily favored recently. However training deep learning models to solve each task separately may be sub-optimal - resource intensive and without the benefit of feature sharing. Here, we propose a new end-to-end 3D deep convolutional neural net (DCNN), called NoduleNet, to solve nodule detection, false positive reduction and nodule segmentation jointly in a multi-task fashion. To avoid friction between different tasks and encourage feature diversification, we incorporate two major design tricks: 1) decoupled feature maps for nodule detection and false positive reduction, and 2) a segmentation refinement subnet for increasing the precision of nodule segmentation. Extensive experiments on the large-scale LIDC dataset demonstrate that the multi-task training is highly beneficial, improving the nodule detection accuracy by 10.27%, compared to the baseline model trained to only solve the nodule detection task. We also carry out systematic ablation studies to highlight contributions from each of the added components. Code is available at this https URL.

Includes members:
  Member "an end to end framework for integrated pulmonary nodule detection and false positive reduction":
    also connected through other hyperedges:
      (none)
  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 "automated pulmonary nodule detection using 3d deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "two stage convolutional neural network architecture for lung nodule detection")
  Member "revisiting rcnn on awakening the classification power of faster rcnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting the sibling head in object detector")
  Member "joint learning for pulmonary nodule segmentation attributes and malignancy prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "shape and margin aware lung nodule classification in low dose ct images via soft activation mapping")
  Member "s4nd single shot single scale lung nodule detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a 3d probabilistic deep learning system for detection and diagnosis of lung cancer using low dose ct scans")
  Member "iw net an automatic and minimalistic interactive lung nodule segmentation deep network":
    also connected through other hyperedges:
      (none)
  Member "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "shape and margin aware lung nodule classification in low dose ct images via soft activation mapping")

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)