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: "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"
  Abstract: In this work, we present a fully automated lung CT cancer diagnosis system, DeepLung. DeepLung contains two parts, nodule detection and classification. Considering the 3D nature of lung CT data, two 3D networks are designed for the nodule detection and classification respectively. Specifically, a 3D Faster R-CNN is designed for nodule detection with a U-net-like encoder-decoder structure to effectively learn nodule features. For nodule classification, gradient boosting machine (GBM) with 3D dual path network (DPN) features is proposed. The nodule classification subnetwork is validated on a public dataset from LIDC-IDRI, on which it achieves better performance than state-of-the-art approaches, and surpasses the average performance of four experienced doctors. For the DeepLung system, candidate nodules are detected first by the nodule detection subnetwork, and nodule diagnosis is conducted by the classification subnetwork. Extensive experimental results demonstrate the DeepLung is comparable to the experienced doctors both for the nodule-level and patient-level diagnosis on the LIDC-IDRI dataset.

Connected through hyperedge HE-1 (induced by source paper "shape and margin aware lung nodule classification in low dose ct images via soft activation mapping"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "competitive performance of a modularized deep neural network compared to commercial algorithms for low dose ct image reconstruction"
    - "joint learning for pulmonary nodule segmentation attributes and malignancy prediction"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "brain tumor segmentation with deep neural networks"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "dual path networks"

Connected through hyperedge HE-2 (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"
    - "automated pulmonary nodule detection using 3d deep convolutional neural networks"
    - "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"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "sisc end to end interpretable discovery radiomics driven lung cancer prediction via stacked interpretable sequencing cells"):
  with members:
    - "an interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "diagnostic classification of lung nodules using 3d neural networks"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"
    - "opening the black box of financial ai with clear trade a class enhanced attentive response approach for explaining and visualizing deep learning driven stock market prediction"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "characterization of lung nodule malignancy using hybrid shape and appearance features"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-4 (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"
    - "automated pulmonary nodule detection using 3d deep convolutional neural networks"
    - "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"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "radiomics strategies for risk assessment of tumour failure in head and neck cancer"
    - "squeeze and excitation networks"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "tversky loss function for image segmentation using 3d fully convolutional deep networks"

Connected through hyperedge HE-6 (induced by source paper "automatic pulmonary lobe segmentation using deep learning"):
  with members:
    - "automated pulmonary nodule detection using 3d deep convolutional neural networks"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "focal loss for dense object detection"
    - "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-7 (induced by source paper "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"):
  with members:
    - "automated pulmonary nodule detection using 3d deep convolutional neural networks"
    - "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"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "cased curriculum adaptive sampling for extreme data imbalance"

Connected through hyperedge HE-8 (induced by source paper "diagnostic classification of lung nodules using 3d neural networks"):
  with members:
    - "densely connected convolutional networks"
    - "benign malignant lung nodule classification with geometric and appearance histogram features"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)