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 "automatic generation of adversarial examples for interpreting malware classifiers"):
  Abstract: Recent advances in adversarial attacks have shown that machine learning#N#classifiers based on static analysis are vulnerable to adversarial attacks.#N#However, real-world antivirus systems do not rely only on static classifiers,#N#thus many of these static evasions get detected by dynamic analysis whenever#N#the malware runs. The real question is to what extent these adversarial attacks#N#are actually harmful to the real users? In this paper, we propose a systematic#N#framework to create and evaluate realistic adversarial malware to evade#N#real-world systems. We propose new adversarial attacks against real-world#N#antivirus systems based on code randomization and binary manipulation, and use#N#our framework to perform the attacks on 1000 malware samples and test four#N#commercial antivirus software and two open-source classifiers. We demonstrate#N#that the static detectors of real-world antivirus can be evaded 24.3%-41.9% of#N#the cases and often by changing only one byte. We also find that the#N#adversarial attacks are transferable between different antivirus up to 16% of#N#the cases. We also tested the efficacy of the complete (i.e. static + dynamic)#N#classifiers in protecting users. While most of the commercial antivirus use#N#their dynamic engines to protect the users' device when the static classifiers#N#are evaded, we are the first to demonstrate that for one commercial antivirus,#N#static evasions can also evade the offline dynamic detectors and infect users'#N#machines. Our framework can also help explain which features are responsible#N#for evasion and thus can help improve the robustness of malware detectors.

Includes members:
  Member "understanding the efficacy reliability and resiliency of computer vision techniques for malware detection and future research directions":
    also connected through other hyperedges:
      (none)
  Member "atmpa attacking machine learning based malware visualization detection methods via adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial examples opportunities and challenges")
  Member "on defending against label flipping attacks on malware detection systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")
  Member "adversarial samples on android malware detection systems for iot systems":
    also connected through other hyperedges:
      (none)
  Member "copycat practical adversarial attacks on visualization based malware detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "computer systems have 99 problems let s not make machine learning another one")
  Member "generation evaluation of adversarial examples for malware obfuscation":
    also connected through other hyperedges:
      (none)
  Member "examining adversarial learning against graph based iot malware detection systems":
    also connected through other hyperedges:
      (none)
  Member "explaining vulnerabilities of deep learning to adversarial malware binaries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "waf a mole evading web application firewalls through adversarial machine learning")

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)