human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "no surprises training robust lung nodule detection for low dose ct scans by augmenting with adversarial attacks"):
  Abstract: Detecting malignant pulmonary nodules at an early stage can allow medical interventions which increases the survival rate of lung cancer patients. Using computer vision techniques to detect nodules can improve the sensitivity and the speed of interpreting chest CT for lung cancer screening. Many studies have used CNNs to detect nodule candidates. Though such approaches have been shown to outperform the conventional image processing based methods regarding the detection accuracy, CNNs are also known to be limited to generalize on under-represented samples in the training set and prone to imperceptible noise perturbations. Such limitations can not be easily addressed by scaling up the dataset or the models. In this work, we propose to add adversarial synthetic nodules and adversarial attack samples to the training data to improve the generalization and the robustness of the lung nodule detection systems. In order to generate hard examples of nodules from a differentiable nodule synthesizer, we use projected gradient descent (PGD) to search the latent code within a bounded neighbourhood that would generate nodules to decrease the detector response. To make the network more robust to unanticipated noise perturbations, we use PGD to search for noise patterns that can trigger the network to give over-confident mistakes. By evaluating on two different benchmark datasets containing consensus annotations from three radiologists, we show that the proposed techniques can improve the detection performance on real CT data. To understand the limitations of both the conventional networks and the proposed augmented networks, we also perform stress-tests on the false positive reduction networks by feeding different types of artificially produced patches. We show that the augmented networks are more robust to both under-represented nodules as well as resistant to noise perturbations.

Includes members:
  Member "understanding adversarial attacks on deep learning based medical image analysis systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deceiving image to image translation networks for autonomous driving with adversarial perturbations")
  Member "towards neural networks that provably know when they don t know":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "natural adversarial examples")
  Member "correlation via synthesis end to end nodule image generation and radiogenomic map learning based on generative adversarial network":
    also connected through other hyperedges:
      (none)
  Member "data augmentation with manifold exploring geometric transformations for increased performance and robustness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "population based augmentation efficient learning of augmentation policy schedules")
  Member "generative adversarial network in medical imaging a review":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "medgan medical image translation using gans")
  Member "batch normalization is a cause of adversarial vulnerability":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "batch norm with entropic regularization turns deterministic autoencoders into generative models")
  Member "synthesizing diverse lung nodules wherever massively 3d multi conditional gan based ct image augmentation for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bridging the gap between ai and healthcare sides towards developing clinically relevant ai powered diagnosis systems")
  Member "quantifying and leveraging classification uncertainty for chest radiograph assessment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "model based and data driven strategies in medical image computing")

Question: Please tell me which class the source paper 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)