human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "invisible backdoor attacks against deep neural networks"):
  Abstract: Deep neural networks (DNNs) have been proven vulnerable to backdoor attacks, where hidden features (patterns) trained to a normal model, and only activated by some specific input (called triggers), trick the model into producing unexpected behavior. In this paper, we design an optimization framework to create covert and scattered triggers for backdoor attacks, \textit{invisible backdoors}, where triggers can amplify the specific neuron activation, while being invisible to both backdoor detection methods and human inspection. We use the Perceptual Adversarial Similarity Score (PASS)~\cite{rozsa2016adversarial} to define invisibility for human users and apply $L_2$ and $L_0$ regularization into the optimization process to hide the trigger within the input data. We show that the proposed invisible backdoors can be fairly effective across various DNN models as well as three datasets CIFAR-10, CIFAR-100, and GTSRB, by measuring their attack success rates and invisibility scores.

Includes members:
  Member "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "februus input purification defense against trojan attacks on deep neural network systems")
  Member "evading defenses to transferable adversarial examples by translation invariant attacks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "using honeypots to catch adversarial attacks on neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models")
  Member "is feature selection secure against training data poisoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")
  Member "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models")
  Member "adversarial examples are not easily detected bypassing ten detection methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "badnets identifying vulnerabilities in the machine learning model supply chain":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "adversarial diversity and hard positive generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "conaml constrained adversarial machine learning for cyber physical systems")

Question: Please tell me which class the source paper 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)