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: "mtdeep boosting the security of deep neural nets against adversarial attacks with moving target defense"
  Abstract: Recent works on gradient-based attacks and universal perturbations can adversarially modify images to bring down the accuracy of state-of-the-art classification techniques based on deep neural networks to as low as 10\% on popular datasets like MNIST and ImageNet. The design of general defense strategies against a wide range of such attacks remains a challenging problem. In this paper, we derive inspiration from recent advances in the fields of cybersecurity and multi-agent systems and propose to use the concept of Moving Target Defense (MTD) for increasing the robustness of a set of deep networks against such adversarial attacks. To this end, we formalize and exploit the notion of differential immunity of an ensemble of networks to specific attacks. To classify an input image, a trained network is picked from this set of networks by formulating the interaction between a Defender (who hosts the classification networks) and their (Legitimate and Malicious) Users as a repeated Bayesian Stackelberg Game (BSG). We empirically show that our approach, MTDeep reduces misclassification on perturbed images for MNIST and ImageNet datasets while maintaining high classification accuracy on legitimate test images. Lastly, we demonstrate that our framework can be used in conjunction with any existing defense mechanism to provide more resilience to adversarial attacks than those defense mechanisms by themselves.

Connected through hyperedge HE-1 (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"
    - "breaking transferability of adversarial samples with randomness"
    - "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"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "moving target defense for deep visual sensing against adversarial examples"):
  with members:
    - "simple physical adversarial examples against end to end autonomous driving models"
    - "on evaluating adversarial robustness"
    - "a dual approach to scalable verification of deep networks"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-3 (induced by source paper "toward proactive adaptive defense a survey on moving target defense"):
  with members:
    - "modeling and analysis of integrated proactive defense mechanisms for internet of things"
    - "moving target defense for deep visual sensing against adversarial examples"
    - "defending via strategic ml selection"
    - "a survey of moving target defenses for network security"
    - "frvm flexible random virtual ip multiplexing in software defined networks"
    - "cost benefit analysis of moving target defense in power grids"
    - "markov game modeling of moving target defense for strategic detection of threats in cloud networks"
    - "a game theoretic taxonomy and survey of defensive deception for cybersecurity and privacy"

Connected through hyperedge HE-4 (induced by source paper "a survey of moving target defenses for network security"):
  with members:
    - "toward a theory of cyber attacks"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "adaptive mtd security using markov game modeling"
    - "trufl distributed trust management framework in sdn"
    - "markov game modeling of moving target defense for strategic detection of threats in cloud networks"
    - "moving target defense for web applications using bayesian stackelberg games"
    - "software defined networking a comprehensive survey"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-5 (induced by source paper "motivating the rules of the game for adversarial example research"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "machine vs machine minimax optimal defense against adversarial examples"
    - "scaling provable adversarial defenses"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "stochastic activation pruning for robust adversarial defense"
    - "adversarial patch"
    - "bidirectional learning for robust neural networks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning for autonomous defence in software defined networking"):
  with members:
    - "semantic adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "certified defenses for data poisoning attacks"
    - "enhancing robustness of machine learning systems via data transformations"

Connected through hyperedge HE-7 (induced by source paper "stochastic combinatorial ensembles for defending against adversarial examples"):
  with members:
    - "adversarial spheres"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "magnet a two pronged defense against adversarial examples"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "a boundary tilting persepective on the phenomenon of adversarial examples"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "delving into transferable adversarial examples and black box attacks"

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)