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: "breaking transferability of adversarial samples with randomness"
  Abstract: We investigate the role of transferability of adversarial attacks in the observed vulnerabilities of Deep Neural Networks (DNNs). We demonstrate that introducing randomness to the DNN models is sufficient to defeat adversarial attacks, given that the adversary does not have an unlimited attack budget. Instead of making one specific DNN model robust to perfect knowledge attacks (a.k.a, white box attacks), creating randomness within an army of DNNs completely eliminates the possibility of perfect knowledge acquisition, resulting in a significantly more robust DNN ensemble against the strongest form of attacks. We also show that when the adversary has an unlimited budget of data perturbation, all defensive techniques would eventually break down as the budget increases. Therefore, it is important to understand the game saddle point where the adversary would not further pursue this endeavor. #R##N#Furthermore, we explore the relationship between attack severity and decision boundary robustness in the version space. We empirically demonstrate that by simply adding a small Gaussian random noise to the learned weights, a DNN model can increase its resilience to adversarial attacks by as much as 74.2%. More importantly, we show that by randomly activating/revealing a model from a pool of pre-trained DNNs at each query request, we can put a tremendous strain on the adversary's attack strategies. We compare our randomization techniques to the Ensemble Adversarial Training technique and show that our randomization techniques are superior under different attack budget constraints.

Connected through hyperedge HE-1 (induced by source paper "on the security of randomized defenses against adversarial samples"):
  with members:
    - "breaking certified defenses semantic adversarial examples with spoofed robustness certificates"
    - "certified adversarial robustness via randomized smoothing"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "on evaluating adversarial robustness"
    - "training verified learners with learned verifiers"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "are adversarial examples inevitable"

Connected through hyperedge HE-2 (induced by source paper "advms a multi source multi cost defense against adversarial attacks"):
  with members:
    - "protecting neural networks with hierarchical random switching towards better robustness accuracy trade off for stochastic defenses"
    - "adversarial t shirt evading person detectors in a physical world"
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "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"
    - "mtdeep boosting the security of deep neural nets against adversarial attacks with moving target defense"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "ensemble noise simulation to handle uncertainty about gradient based adversarial attacks"):
  with members:
    - "transferable adversarial robustness using adversarially trained autoencoders"
    - "a computationally efficient method for defending adversarial deep learning attacks"
    - "noise flooding for detecting audio adversarial examples against automatic speech recognition"
    - "training multi task adversarial network for extracting noise robust speaker embedding"
    - "boosting noise robustness of acoustic model via deep adversarial training"
    - "combatting adversarial attacks through denoising and dimensionality reduction a cascaded autoencoder approach"
    - "enhancing robustness of machine learning systems via data transformations"
    - "magnet a two pronged defense against adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "terminal brain damage exposing the graceless degradation in deep neural networks under hardware fault attacks"):
  with members:
    - "page cache attacks"
    - "scalable methods for 8 bit training of neural networks"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "sever a robust meta algorithm for stochastic optimization"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "training deep neural networks with 8 bit floating point numbers"
    - "nethammer inducing rowhammer faults through network requests"
    - "poison frogs targeted clean label poisoning attacks on neural networks"

Connected through hyperedge HE-5 (induced by source paper "adversarial defense of image classification using a variational auto encoder"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "efficient defenses against adversarial attacks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

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)