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: "are odds really odd bypassing statistical detection of adversarial examples"
  Abstract: Deep learning classifiers are known to be vulnerable to adversarial examples. A recent paper presented at ICML 2019 proposed a statistical test detection method based on the observation that logits of noisy adversarial examples are biased toward the true class. The method is evaluated on CIFAR-10 dataset and is shown to achieve 99% true positive rate (TPR) at only 1% false positive rate (FPR). In this paper, we first develop a classifier-based adaptation of the statistical test method and show that it improves the detection performance. We then propose Logit Mimicry Attack method to generate adversarial examples such that their logits mimic those of benign images. We show that our attack bypasses both statistical test and classifier-based methods, reducing their TPR to less than 2:2% and 1:6%, respectively, even at 5% FPR. We finally show that a classifier-based detector that is trained with logits of mimicry adversarial examples can be evaded by an adaptive attacker that specifically targets the detector. Furthermore, even a detector that is iteratively trained to defend against adaptive attacker cannot be made robust, indicating that statistics of logits cannot be used to detect adversarial examples.

Connected through hyperedge HE-1 (induced by source paper "raid randomized adversarial input detection for neural networks"):
  with members:
    - "when explainability meets adversarial learning detecting adversarial examples using shap signatures"
    - "deepsmartfuzzer reward guided test generation for deep learning"
    - "deepfault fault localization for deep neural networks"
    - "a new defense against adversarial images turning a weakness into a strength"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "on evaluating adversarial robustness"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "detecting and diagnosing adversarial images with class conditional capsule reconstructions"):
  with members:
    - "mnist c a robustness benchmark for computer vision"
    - "deepcaps going deeper with capsule networks"
    - "on the vulnerability of capsule networks to adversarial attacks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "adversarial attacks and defences competition"
    - "towards the first adversarially robust neural network model on mnist"
    - "adversarial spheres"

Connected through hyperedge HE-3 (induced by source paper "deflecting adversarial attacks"):
  with members:
    - "detecting and diagnosing adversarial images with class conditional capsule reconstructions"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "on evaluating adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "dynamic routing between capsules"
    - "pixeldefend leveraging generative models to understand and defend against 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.LG(Machine Learning)