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: "towards automatic pulmonary nodule management in lung cancer screening with deep learning"
  Abstract: The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.

Connected through hyperedge HE-1 (induced by source paper "evolving the pulmonary nodules diagnosis from classical approaches to deep learning aided decision support three decades development course and future prospect"):
  with members:
    - "adversarial attacks against medical deep learning systems"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "computer aided diagnosis of lung nodule using gradient tree boosting and bayesian optimization"
    - "supervised and unsupervised tumor characterization in the deep learning era"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "a survey on deep learning in medical image analysis"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "a berkeley view of systems challenges for ai"

Connected through hyperedge HE-2 (induced by source paper "from handcrafted to deep learning based cancer radiomics challenges and opportunities"):
  with members:
    - "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
    - "brain tumor type classification via capsule networks"
    - "dynamic routing between capsules"
    - "radiomics strategies for risk assessment of tumour failure in head and neck cancer"
    - "prostate cancer diagnosis using deep learning with 3d multiparametric mri"
    - "understanding convolution for semantic segmentation"
    - "a survey on deep learning in medical image analysis"
    - "discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection"

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"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "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 "3d convolutional neural networks for tumor segmentation using long range 2d context"):
  with members:
    - "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge"
    - "3d consistent robust segmentation of cardiac images by deep learning with spatial propagation"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "brain tumor segmentation with deep neural networks"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "supervised and unsupervised tumor characterization in the deep learning era"):
  with members:
    - "s4nd single shot single scale lung nodule detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "multi scale gradual integration cnn for false positive reduction in pulmonary nodule detection"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "multi stage neural networks with single sided classifiers for false positive reduction and its evaluation using lung x ray ct images"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "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 convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "feature pyramid networks for object detection"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"

Connected through hyperedge HE-7 (induced by source paper "unsupervised and semi supervised learning with categorical generative adversarial networks assisted by wasserstein distance for dermoscopy image classification"):
  with members:
    - "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"
    - "an overview of melanoma detection in dermoscopy images using image processing and machine learning"
    - "regularization with stochastic transformations and perturbations for deep semi supervised learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "semi supervised learning with ladder networks"

Connected through hyperedge HE-8 (induced by source paper "computer aided diagnosis of lung nodule using gradient tree boosting and bayesian optimization"):
  with members:
    - "best practices for applying deep learning to novel applications"
    - "xgboost a scalable tree boosting system"

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)