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: "characterization of lung nodule malignancy using hybrid shape and appearance features"
  Abstract: Computed tomography imaging is a standard modality for detecting and assessing lung cancer. In order to evaluate the malignancy of lung nodules, clinical practice often involves expert qualitative ratings on several criteria describing a nodule's appearance and shape. Translating these features for computer-aided diagnostics is challenging due to their subjective nature and the difficulties in gaining a complete description. In this paper, we propose a computerized approach to quantitatively evaluate both appearance distinctions and 3D surface variations. Nodule shape was modeled and parameterized using spherical harmonics, and appearance features were extracted using deep convolutional neural networks. Both sets of features were combined to estimate the nodule malignancy using a random forest classifier. The proposed algorithm was tested on the publicly available Lung Image Database Consortium dataset, achieving high accuracy. By providing lung nodule characterization, this method can provide a robust alternative reference opinion for lung cancer diagnosis.

Connected through hyperedge HE-1 (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"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"
    - "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"

Connected through hyperedge HE-2 (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"
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "understanding neural networks through deep visualization"

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"
    - "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"
    - "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 "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"
    - "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"
    - "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"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

Connected through hyperedge HE-5 (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"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"
    - "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"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"

Connected through hyperedge HE-6 (induced by source paper "joint learning for pulmonary nodule segmentation attributes and malignancy prediction"):
  with members:
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "tumornet lung nodule characterization using multi view convolutional neural network with gaussian process"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "interpretable spiculation quantification for lung cancer screening"):
  with members:
    - "spherical parameterization balancing angle and area distortions"
    - "discovery radiomics for pathologically proven computed tomography lung cancer prediction"

Connected through hyperedge HE-8 (induced by source paper "risk stratification of lung nodules using 3d cnn based multi task learning"):
  with members:
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "learning spatiotemporal features with 3d convolutional networks"

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)