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: "generalizability vs robustness adversarial examples for medical imaging"
  Abstract: In this paper, for the first time, we propose an evaluation method for deep learning models that assesses the performance of a model not only in an unseen test scenario, but also in extreme cases of noise, outliers and ambiguous input data. To this end, we utilize adversarial examples, images that fool machine learning models, while looking imperceptibly different from original data, as a measure to evaluate the robustness of a variety of medical imaging models. Through extensive experiments on skin lesion classification and whole brain segmentation with state-of-the-art networks such as Inception and UNet, we show that models that achieve comparable performance regarding generalizability may have significant variations in their perception of the underlying data manifold, leading to an extensive performance gap in their robustness.

Connected through hyperedge HE-1 (induced by source paper "no surprises training robust lung nodule detection for low dose ct scans by augmenting with adversarial attacks"):
  with members:
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "towards neural networks that provably know when they don t know"
    - "correlation via synthesis end to end nodule image generation and radiogenomic map learning based on generative adversarial network"
    - "data augmentation with manifold exploring geometric transformations for increased performance and robustness"
    - "generative adversarial network in medical imaging a review"
    - "batch normalization is a cause of adversarial vulnerability"
    - "synthesizing diverse lung nodules wherever massively 3d multi conditional gan based ct image augmentation for object detection"
    - "quantifying and leveraging classification uncertainty for chest radiograph assessment"

Connected through hyperedge HE-2 (induced by source paper "metric learning for adversarial robustness"):
  with members:
    - "hardness aware deep metric learning"
    - "are labels required for improving adversarial robustness"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "an analysis of the t sne algorithm for data visualization"
    - "formal security analysis of neural networks using symbolic intervals"
    - "logit pairing methods can fool gradient based attacks"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-3 (induced by source paper "adversarial attacks against medical deep learning systems"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "a dual approach to scalable verification of deep networks"
    - "adversarial patch"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "adversarial logit pairing"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

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)