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 "feature level malware obfuscation in deep learning"):
  Abstract: We consider the problem of detecting malware with deep learning models, where the malware may be combined with significant amounts of benign code. Examples of this include piggybacking and trojan horse attacks on a system, where malicious behavior is hidden within a useful application. Such added flexibility in augmenting the malware enables significantly more code obfuscation. Hence we focus on the use of static features, particularly Intents, Permissions, and API calls, which we presume cannot be ultimately hidden from the Android system, but only augmented with yet more such features. We first train a deep neural network classifier for malware classification using features of benign and malware samples. Then we demonstrate a steep increase in false negative rate (i.e., attacks succeed), simply by randomly adding features of a benign app to malware. Finally we test the use of data augmentation to harden the classifier against such attacks. We find that for API calls, it is possible to reject the vast majority of attacks, where using Intents or Permissions is less successful.

Includes members:
  Member "adversarial attacks and defenses in images graphs and text a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "effectiveness of adversarial examples and defenses for malware classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "a large scale investigation of obfuscation use in google play":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spellbound defending against package typosquatting")
  Member "adversarial malware binaries evading deep learning for malware detection in executables":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "exploring adversarial examples in malware detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "deep learning at the shallow end malware classification for non domain experts":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scratch that an evolution based adversarial attack against neural networks")
  Member "feature squeezing detecting adversarial examples in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "motivating the rules of the game for adversarial example research":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")

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)