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 "conaml constrained adversarial machine learning for cyber physical systems"):
  Abstract: Recent research demonstrated that the superficially well-trained machine learning (ML) models are highly vulnerable to adversarial examples. As ML techniques are rapidly employed in cyber-physical systems (CPSs), the security of these applications is of concern. However, current studies on adversarial machine learning (AML) mainly focus on computer vision and related fields. The risks the adversarial examples can bring to the CPS applications have not been well investigated. In particular, due to the distributed property of data sources and the inherent physical constraints imposed by CPSs, the widely-used threat models in previous research and the state-of-the-art AML algorithms are no longer practical when applied to CPS applications. #R##N#We study the vulnerabilities of ML applied in CPSs by proposing Constrained Adversarial Machine Learning (ConAML), which generates adversarial examples used as ML model input that meet the intrinsic constraints of the physical systems. We first summarize the difference between AML in CPSs and AML in existing cyber systems and propose a general threat model for ConAML. We then design a best-effort search algorithm to iteratively generate adversarial examples with linear physical constraints. As proofs of concept, we evaluate the vulnerabilities of ML models used in the electric power grid and water treatment systems. The results show that our ConAML algorithms can effectively generate adversarial examples which significantly decrease the performance of the ML models even under practical physical constraints.

Includes members:
  Member "textbugger generating adversarial text against real world applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks")
  Member "dynamic detection of false data injection attack in smart grid using deep learning":
    also connected through other hyperedges:
      (none)
  Member "adversarial attacks and defences a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ai safety state of the field through quantitative lens")
  Member "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a system level behavioral detection framework for compromised cps devices smart grid case")
  Member "adversarial regression for detecting attacks in cyber physical systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "can t boil this frog robustness of online trained autoencoder based anomaly detectors to adversarial poisoning attacks")
  Member "no need to worry about adversarial examples in object detection in autonomous vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "a deep learning based framework for conducting stealthy attacks in industrial control systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "can t boil this frog robustness of online trained autoencoder based anomaly detectors to adversarial poisoning attacks")
  Member "anomaly detection for a water treatment system using unsupervised machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intrusion detection for industrial control systems evaluation analysis and adversarial attacks")

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)