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 "ten ai stepping stones for cybersecurity"):
  Abstract: With the turmoil in cybersecurity and the mind-blowing advances in AI, it is only natural that cybersecurity practitioners consider further employing learning techniques to help secure their organizations and improve the efficiency of their security operation centers. But with great fears come great opportunities for both the good and the evil, and a myriad of bad deals. This paper discusses ten issues in cybersecurity that hopefully will make it easier for practitioners to ask detailed questions about what they want from an AI system in their cybersecurity operations. We draw on the state of the art to provide factual arguments for a discussion on well-established AI in cybersecurity issues, including the current scope of AI and its application to cybersecurity, the impact of privacy concerns on the cybersecurity data that can be collected and shared externally to the organization, how an AI decision can be explained to the person running the operations center, and the implications of the adversarial nature of cybersecurity in the learning techniques. We then discuss the use of AI by attackers on a level playing field including several issues in an AI battlefield, and an AI perspective on the old cat-and-mouse game including how the adversary may assess your AI power.

Includes members:
  Member "machine learning ddos detection for consumer internet of things devices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "iot behavioral monitoring via network traffic analysis")
  Member "adversarial deep learning for robust detection of binary encoded malware":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effectiveness of adversarial examples and defenses for malware classification")
  Member "cache telepathy leveraging shared resource attacks to learn dnn architectures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural network model extraction attacks in edge devices by hearing architectural hints")
  Member "provable limitations of deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "poly time universality and limitations of deep learning")
  Member "adversarial malware binaries evading deep learning for malware detection in executables":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "a first look at deep learning apps on smartphones":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural architecture search over decentralized data")
  Member "prada protecting against dnn model stealing attacks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nass optimizing secure inference via neural architecture search")
  Member "logan membership inference attacks against generative models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "wireless federated learning with local differential privacy")

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)