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: "risk stratification of lung nodules using 3d cnn based multi task learning"
  Abstract: Risk stratification of lung nodules is a task of primary importance in lung cancer diagnosis. Any improvement in robust and accurate nodule characterization can assist in identifying cancer stage, prognosis, and improving treatment planning. In this study, we propose a 3D Convolutional Neural Network (CNN) based nodule characterization strategy. With a completely 3D approach, we utilize the volumetric information from a CT scan which would be otherwise lost in the conventional 2D CNN based approaches. In order to address the need for a large amount of training data for CNN, we resort to transfer learning to obtain highly discriminative features. Moreover, we also acquire the task dependent feature representation for six high-level nodule attributes and fuse this complementary information via a Multi-task learning (MTL) framework. Finally, we propose to incorporate potential disagreement among radiologists while scoring different nodule attributes in a graph regularized sparse multi-task learning. We evaluated our proposed approach on one of the largest publicly available lung nodule datasets comprising 1018 scans and obtained state-of-the-art results in regressing the malignancy scores.

Connected through hyperedge HE-1 (induced by source paper "gated dilated networks for lung nodule classification in ct scans"):
  with members:
    - "lung nodule classification using deep local global networks"
    - "an interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification"
    - "dilated residual networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "aggregated residual transformations for deep neural networks"
    - "identity mappings in deep residual networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "neural machine translation in linear time"

Connected through hyperedge HE-2 (induced by source paper "a 3d probabilistic deep learning system for detection and diagnosis of lung cancer using low dose ct scans"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "attention based deep multiple instance learning"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "towards radiologist level cancer risk assessment in ct lung screening using deep learning"
    - "s4nd single shot single scale lung nodule detection"
    - "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning"
    - "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-3 (induced by source paper "lung and pancreatic tumor characterization in the deep learning era novel supervised and unsupervised learning approaches"):
  with members:
    - "s4nd single shot single scale lung nodule detection"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "towards automatic pulmonary nodule management in lung cancer screening with deep learning"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "characterization of lung nodule malignancy using hybrid shape and appearance features"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "multimodal convolutional neural networks for matching image and sentence"

Connected through hyperedge HE-4 (induced by source paper "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"):
  with members:
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "attention based deep multiple instance learning"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "deep learning with permutation invariant operator for multi instance histopathology classification"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "semi supervised deep learning for fully convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "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"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "dual path networks"
    - "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 multi instance networks with sparse label assignment for whole mammogram classification"

Connected through hyperedge HE-6 (induced by source paper "a collaborative computer aided diagnosis c cad system with eye tracking sparse attentional model and deep learning"):
  with members:
    - "semi supervised multi task learning for lung cancer diagnosis"
    - "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"
    - "tumornet lung nodule characterization using multi view convolutional neural network with gaussian process"
    - "gaze2segment a pilot study for integrating eye tracking technology into medical image segmentation"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "characterization of lung nodule malignancy using hybrid shape and appearance features"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"

Connected through hyperedge HE-7 (induced by source paper "semi supervised multi task learning for lung cancer diagnosis"):
  with members:
    - "how to fool radiologists with generative adversarial networks a visual turing test for lung cancer diagnosis"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"

Connected through hyperedge HE-8 (induced by source paper "crowd disagreement of medical images is informative"):
  with members:
    - "who said what modeling individual labelers improves classification"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"

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)