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 "hacking neural networks a short introduction"):
  Abstract: A large chunk of research on the security issues of neural networks is focused on adversarial attacks. However, there exists a vast sea of simpler attacks one can perform both against and with neural networks. In this article, we give a quick introduction on how deep learning in security works and explore the basic methods of exploitation, but also look at the offensive capabilities deep learning enabled tools provide. All presented attacks, such as backdooring, GPU-based buffer overflows or automated bug hunting, are accompanied by short open-source exercises for anyone to try out.

Includes members:
  Member "deep leakage from gradients":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the future of digital health with federated learning")
  Member "iris verification with convolutional neural network and unit circle layer":
    also connected through other hyperedges:
      (none)
  Member "adversarial attacks and defences competition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nesterov accelerated gradient and scale invariance for adversarial attacks")
  Member "fine pruning defending against backdooring attacks on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "backdooring convolutional neural networks via targeted weight perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rab provable robustness against backdoor attacks")
  Member "invisible mask practical attacks on face recognition with infrared":
    also connected through other hyperedges:
      (none)
  Member "microsoft malware classification challenge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interpreting machine learning malware detectors which leverage n gram analysis")
  Member "a survey of techniques for improving security of gpus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "enabling privacy preserving compute and data intensive computing using heterogeneous trusted execution environment")

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)