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 "idsgan generative adversarial networks for attack generation against intrusion detection"):
  Abstract: As an important tool in security, the intrusion detection system bears the responsibility of the defense to network attacks performed by malicious traffic. Nowadays, with the help of machine learning algorithms, the intrusion detection system develops rapidly. However, the robustness of this system is questionable when it faces the adversarial attacks. To improve the detection system, more potential attack approaches should be researched. In this paper, a framework of the generative adversarial networks, IDSGAN, is proposed to generate the adversarial attacks, which can deceive and evade the intrusion detection system. Considering that the internal structure of the detection system is unknown to attackers, adversarial attack examples perform the black-box attacks against the detection system. IDSGAN leverages a generator to transform original malicious traffic into adversarial malicious traffic. A discriminator classifies traffic examples and simulates the black-box detection system. More significantly, we only modify part of the attacks' nonfunctional features to guarantee the validity of the intrusion. Based on the dataset NSL-KDD, the feasibility of the model is demonstrated to attack many detection systems with different attacks and the excellent results are achieved. Moreover, the robustness of IDSGAN is verified by changing the amount of the unmodified features.

Includes members:
  Member "adversarial deep learning for robust detection of binary encoded malware":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effectiveness of adversarial examples and defenses for malware classification")
  Member "black box attacks against rnn based malware detection algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "adversarial examples are not easily detected bypassing ten detection methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "generative adversarial trainer defense to adversarial perturbations with gan":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distortion agnostic deep watermarking")
  Member "generating adversarial malware examples for black box attacks based on gan":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "adversarial perturbations against deep neural networks for malware classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the security of randomized defenses against adversarial samples")
  Member "photo realistic single image super resolution using a generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)