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: "automated pulmonary nodule detection using 3d deep convolutional neural networks"
  Abstract: Early detection of pulmonary nodules in computed tomography (CT) images is essential for successful outcomes among lung cancer patients. Much attention has been given to deep convolutional neural network (DCNN)-based approaches to this task, but models have relied at least partly on 2D or 2.5D components for inherently 3D data. In this paper, we introduce a novel DCNN approach, consisting of two stages, that is fully three-dimensional end-to-end and utilizes the state-of-the-art in object detection. First, nodule candidates are identified with a U-Net-inspired 3D Faster R-CNN trained using online hard negative mining. Second, false positive reduction is performed by 3D DCNN classifiers trained on difficult examples produced during candidate screening. Finally, we introduce a method to ensemble models from both stages via consensus to give the final predictions. By using this framework, we ranked first of 2887 teams in Season One of Alibaba's 2017 TianChi AI Competition for Healthcare.

Connected through hyperedge HE-1 (induced by source paper "two stage convolutional neural network architecture for lung nodule detection"):
  with members:
    - "a novel algorithm for segmentation of leukocytes in peripheral blood"
    - "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "s4nd single shot single scale lung nodule detection"
    - "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning"
    - "squeeze and excitation networks"
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "nodulenet decoupled false positive reductionfor pulmonary nodule detection and segmentation"):
  with members:
    - "an end to end framework for integrated pulmonary nodule detection and false positive reduction"
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "joint learning for pulmonary nodule segmentation attributes and malignancy prediction"
    - "s4nd single shot single scale lung nodule detection"
    - "iw net an automatic and minimalistic interactive lung nodule segmentation deep network"
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "an end to end framework for integrated pulmonary nodule detection and false positive reduction"):
  with members:
    - "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"
    - "s4nd single shot single scale lung nodule detection"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "automatic pulmonary lobe segmentation using deep learning"):
  with members:
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "focal loss for dense object detection"
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "a combined deep learning gradient boosting machine framework for fluid intelligence prediction"):
  with members:
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "brain tumor segmentation with deep neural networks"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-6 (induced by source paper "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning"
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules"
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "cased curriculum adaptive sampling for extreme data imbalance"

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)