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: "benign malignant lung nodule classification with geometric and appearance histogram features"
  Abstract: Lung cancer accounts for the highest number of cancer deaths globally. Early diagnosis of lung nodules is very important to reduce the mortality rate of patients by improving the diagnosis and treatment of lung cancer. This work proposes an automated system to classify lung nodules as malignant and benign in CT images. It presents extensive experimental results using a combination of geometric and histogram lung nodule image features and different linear and non-linear discriminant classifiers. The proposed approach is experimentally validated on the LIDC-IDRI public lung cancer screening thoracic computed tomography (CT) dataset containing nodule level diagnostic data. The obtained results are very encouraging correctly classifying 82% of malignant and 93% of benign nodules on unseen test data at best.

Connected through hyperedge HE-1 (induced by source paper "diagnostic classification of lung nodules using 3d neural networks"):
  with members:
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"
    - "densely connected convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "highly accurate model for prediction of lung nodule malignancy with ct scans"):
  with members:
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "discovery radiomics for computed tomography cancer detection"

Connected through hyperedge HE-3 (induced by source paper "lung cancer screening using adaptive memory augmented recurrent networks"):
  with members:
    - "fast capsnet for lung cancer screening"
    - "toward robustness against label noise in training deep discriminative neural networks"
    - "one shot learning with memory augmented neural networks"
    - "matching networks for one shot learning"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep convolutional networks for pancreas segmentation in ct imaging"

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)