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: "tumornet lung nodule characterization using multi view convolutional neural network with gaussian process"
  Abstract: Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a fast and robust computer aided system. In this work, we propose an end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for nodule characterization. First, we use median intensity projection to obtain a 2D patch corresponding to each dimension. The three images are then concatenated to form a tensor, where the images serve as different channels of the input image. In order to increase the number of training samples, we perform data augmentation by scaling, rotating and adding noise to the input image. The trained network is used to extract features from the input image followed by a Gaussian Process (GP) regression to obtain the malignancy score. We also empirically establish the significance of different high level nodule attributes such as calcification, sphericity and others for malignancy determination. These attributes are found to be complementary to the deep multi-view CNN features and a significant improvement over other methods is obtained.

Connected through hyperedge HE-1 (induced by source paper "shape and margin aware lung nodule classification in low dose ct images via soft activation mapping"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "competitive performance of a modularized deep neural network compared to commercial algorithms for low dose ct image reconstruction"
    - "joint learning for pulmonary nodule segmentation attributes and malignancy prediction"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "brain tumor segmentation with deep neural networks"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "dual path networks"

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"
    - "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-3 (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"
    - "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-4 (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"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "characterization of lung nodule malignancy using hybrid shape and appearance features"
    - "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-5 (induced by source paper "how to fool radiologists with generative adversarial networks a visual turing test for lung cancer diagnosis"):
  with members:
    - "unsupervised domain adaptation in brain lesion segmentation with adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial 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)