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 limitations of model uncertainty in adversarial settings"
  Abstract: Machine learning models are vulnerable to adversarial examples: minor perturbations to input samples intended to deliberately cause misclassification. Many defenses have led to an arms race-we thus study a promising, recent trend in this setting, Bayesian uncertainty measures. These measures allow a classifier to provide principled confidence and uncertainty for an input, where the latter refers to how usual the input is. We focus on Gaussian processes (GP), a classifier providing such principled uncertainty and confidence measures. Using correctly classified benign data as comparison, GP's intrinsic uncertainty and confidence deviate for misclassified benign samples and misclassified adversarial examples. We therefore introduce high-confidence-low-uncertainty adversarial examples: adversarial examples crafted maximizing GP confidence and minimizing GP uncertainty. Visual inspection shows HCLU adversarial examples are malicious, and resemble the original rather than the target class. HCLU adversarial examples also transfer to other classifiers. We focus on transferability to other algorithms providing uncertainty measures, and find that a Bayesian neural network confidently misclassifies HCLU adversarial examples. We conclude that uncertainty and confidence, even in the Bayesian sense, can be circumvented by both white-box and black-box attackers.

Connected through hyperedge HE-1 (induced by source paper "adversarial vulnerability bounds for gaussian process classification"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "robustness guarantees for bayesian inference with gaussian processes"
    - "killing four birds with one gaussian process analyzing test time attack vectors on classification"
    - "rogue signs deceiving traffic sign recognition with malicious ads and logos"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "provably minimally distorted adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "statistical guarantees for the robustness of bayesian neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "robustness guarantees for bayesian inference with gaussian processes"
    - "verification of deep probabilistic models"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "a game based approximate verification of deep neural networks with provable guarantees"
    - "feature guided black box safety testing of deep neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "adversarial robustness toolbox v1 0 0"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "robustness verification of tree based models"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "sok security and privacy in machine learning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "magnet a two pronged defense against adversarial examples"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"

Connected through hyperedge HE-4 (induced by source paper "killing four birds with one gaussian process analyzing test time attack vectors on classification"):
  with members:
    - "adversarial vulnerability for any classifier"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "darts deceiving autonomous cars with toxic signs"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

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.CR(Cryptography and Security)