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: "diagnostic classification of lung nodules using 3d neural networks"
  Abstract: Lung cancer is the leading cause of cancer-related death worldwide. Early diagnosis of pulmonary nodules in Computed Tomography (CT) chest scans provides an opportunity for designing effective treatment and making financial and care plans. In this paper, we consider the problem of diagnostic classification between benign and malignant lung nodules in CT images, which aims to learn a direct mapping from 3D images to class labels. To achieve this goal, four two-pathway Convolutional Neural Networks (CNN) are proposed, including a basic 3D CNN, a novel multi-output network, a 3D DenseNet, and an augmented 3D DenseNet with multi-outputs. These four networks are evaluated on the public LIDC-IDRI dataset and outperform most existing methods. In particular, the 3D multi-output DenseNet (MoDenseNet) achieves the state-of-the-art classification accuracy on the task of end-to-end lung nodule diagnosis. In addition, the networks pretrained on the LIDC-IDRI dataset can be further extended to handle smaller datasets using transfer learning. This is demonstrated on our dataset with encouraging prediction accuracy in lung nodule classification.

Connected through hyperedge HE-1 (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"
    - "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"
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"
    - "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-2 (induced by source paper "compnet complementary segmentation network for brain mri extraction"):
  with members:
    - "brain extraction from normal and pathological images a joint pca image reconstruction approach"
    - "w net a deep model for fully unsupervised image segmentation"
    - "auto context convolutional neural network for geometry independent brain extraction in magnetic resonance imaging"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)