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: "backdoor embedding in convolutional neural network models via invisible perturbation"
  Abstract: Deep learning models have consistently outperformed traditional machine learning models in various classification tasks, including image classification. As such, they have become increasingly prevalent in many real world applications including those where security is of great concern. Such popularity, however, may attract attackers to exploit the vulnerabilities of the deployed deep learning models and launch attacks against security-sensitive applications. In this paper, we focus on a specific type of data poisoning attack, which we refer to as a {\em backdoor injection attack}. The main goal of the adversary performing such attack is to generate and inject a backdoor into a deep learning model that can be triggered to recognize certain embedded patterns with a target label of the attacker's choice. Additionally, a backdoor injection attack should occur in a stealthy manner, without undermining the efficacy of the victim model. Specifically, we propose two approaches for generating a backdoor that is hardly perceptible yet effective in poisoning the model. We consider two attack settings, with backdoor injection carried out either before model training or during model updating. We carry out extensive experimental evaluations under various assumptions on the adversary model, and demonstrate that such attacks can be effective and achieve a high attack success rate (above $90\%$) at a small cost of model accuracy loss (below $1\%$) with a small injection rate (around $1\%$), even under the weakest assumption wherein the adversary has no knowledge either of the original training data or the classifier model.

Connected through hyperedge HE-1 (induced by source paper "rab provable robustness against backdoor attacks"):
  with members:
    - "dynamic backdoor attacks against machine learning models"
    - "backdoor attacks against transfer learning with pre trained deep learning models"
    - "provable robust learning based on transformation specific smoothing"
    - "on certifying robustness against backdoor attacks via randomized smoothing"
    - "certified robustness to label flipping attacks via randomized smoothing"
    - "certified adversarial robustness via randomized smoothing"
    - "strip a defence against trojan attacks on deep neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"

Connected through hyperedge HE-2 (induced by source paper "generating semantic adversarial examples via feature manipulation"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "magnet a two pronged defense against adversarial examples"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "on the statistical detection of adversarial examples"
    - "generating natural adversarial examples"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-3 (induced by source paper "watch your back backdoor attacks in deep reinforcement learning based autonomous vehicle control systems"):
  with members:
    - "dissipation of stop and go waves via control of autonomous vehicles field experiments"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "spectral signatures in backdoor attacks"
    - "sequential attacks on agents for long term adversarial goals"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "flow a modular learning framework for autonomy in traffic"
    - "vulnerability of deep reinforcement learning to policy induction attacks"

Connected through hyperedge HE-4 (induced by source paper "adversarial learning in statistical classification a comprehensive review of defenses against attacks"):
  with members:
    - "revealing backdoors post training in dnn classifiers via novel inference on optimized perturbations inducing group misclassification"
    - "strip a defence against trojan attacks on deep neural networks"
    - "defensive quantization when efficiency meets robustness"
    - "revealing perceptible backdoors without the training set via the maximum achievable misclassification fraction statistic"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "a mixture model based defense for data poisoning attacks against naive bayes spam filters"

Connected through hyperedge HE-5 (induced by source paper "detecting ai trojans using meta neural analysis"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "exploiting unintended feature leakage in collaborative learning"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sentinet detecting physical attacks against deep learning systems"
    - "backdooring convolutional neural networks via targeted weight perturbations"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"

Connected through hyperedge HE-6 (induced by source paper "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning"):
  with members:
    - "security of deep learning methodologies challenges and opportunities"
    - "how to achieve high classification accuracy with just a few labels a semi supervised approach using sampled packets"
    - "model reuse attacks on deep learning systems"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "deep learning for encrypted traffic classification an overview"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "are you tampering with my data"
    - "the extreme value machine"

Connected through hyperedge HE-7 (induced by source paper "strip a defence against trojan attacks on deep neural networks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "model reuse attacks on deep learning systems"
    - "sentinet detecting physical attacks against deep learning systems"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "blackmarks black box multi bit watermarking for deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "hidden trigger backdoor attacks"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "gotta catch em all using concealed trapdoors to detect adversarial attacks on neural networks"
    - "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning"
    - "poisoning attacks with generative adversarial nets"
    - "using honeypots to catch adversarial attacks on neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "spectral signatures in backdoor 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.CR(Cryptography and Security)