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: "the odds are odd a statistical test for detecting adversarial examples"
  Abstract: We investigate conditions under which test statistics exist that can reliably detect examples, which have been adversarially manipulated in a white-box attack. These statistics can be easily computed and calibrated by randomly corrupting inputs. They exploit certain anomalies that adversarial attacks introduce, in particular if they follow the paradigm of choosing perturbations optimally under p-norm constraints. Access to the log-odds is the only requirement to defend models. We justify our approach empirically, but also provide conditions under which detectability via the suggested test statistics is guaranteed to be effective. In our experiments, we show that it is even possible to correct test time predictions for adversarial attacks with high accuracy.

Connected through hyperedge HE-1 (induced by source paper "on adaptive attacks to adversarial example defenses"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "adversarial example detection and classification with asymmetrical adversarial training"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "mixup inference better exploiting mixup to defend adversarial attacks"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "a new defense against adversarial images turning a weakness into a strength"
    - "resisting adversarial attacks by k winners take all"
    - "rethinking softmax cross entropy loss for adversarial robustness"

Connected through hyperedge HE-2 (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"
    - "are odds really odd bypassing statistical detection of adversarial examples"
    - "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"
    - "on evaluating adversarial robustness"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "an empirical evaluation of perturbation based defenses"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "ppd permutation phase defense against adversarial examples in deep learning"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "the effects of jpeg and jpeg2000 compression on attacks using adversarial examples"
    - "deflecting adversarial attacks with pixel deflection"

Connected through hyperedge HE-4 (induced by source paper "adversarial training is a form of data dependent operator norm regularization"):
  with members:
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "adversarial vulnerability for any classifier"
    - "spectral normalization for generative adversarial networks"
    - "generalizable adversarial training via spectral normalization"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "adversarial perturbations on the perceptual ball"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "interpretable counterfactual explanations guided by prototypes"
    - "universal adversarial perturbations against person re identification"
    - "counterfactual visual explanations"
    - "hardening deep neural networks via adversarial model cascades"
    - "disentangling adversarial robustness and generalization"

Connected through hyperedge HE-6 (induced by source paper "adversarial example detection and classification with asymmetrical adversarial training"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "conditional generative models are not robust"
    - "natural and adversarial error detection using invariance to image transformations"
    - "consistency regularization for generative adversarial networks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "towards the first adversarially robust neural network model on mnist"
    - "large scale gan training for high fidelity natural image synthesis"
    - "poison frogs targeted clean label poisoning attacks on neural networks"

Connected through hyperedge HE-7 (induced by source paper "a new defense against adversarial images turning a weakness into a strength"):
  with members:
    - "simple black box adversarial attacks"
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial vulnerability for any classifier"
    - "are adversarial examples inevitable"
    - "deflecting adversarial attacks with pixel deflection"

Connected through hyperedge HE-8 (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"
    - "are odds really odd bypassing statistical detection of adversarial examples"
    - "on the vulnerability of capsule networks to adversarial attacks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "adversarial attacks and defences competition"
    - "towards the first adversarially robust neural network model on mnist"
    - "adversarial spheres"

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)