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 "a system level behavioral detection framework for compromised cps devices smart grid case"):
  Abstract: Cyber-Physical Systems (CPS) play a significant role in our critical infrastructure networks from power-distribution to utility networks. The emerging smart-grid concept is a compelling critical CPS infrastructure that relies on two-way communications between smart devices to increase efficiency, enhance reliability, and reduce costs. However, compromised devices in the smart grid poses several security challenges. Consequences of propagating fake data or stealing sensitive smart grid information via compromised devices are costly. Hence, early behavioral detection of compromised devices is critical for protecting the smart grid's components and data. To address these concerns, in this paper, we introduce a novel and configurable system-level framework to identify compromised smart grid devices. The framework combines system and function call tracing techniques with signal processing and statistical analysis to detect compromised devices based on their behavioral characteristics. We measure the efficacy of our framework with a realistic smart grid substation testbed that includes both resource-limited and resource-rich devices. In total, using our framework, we analyze six different types of compromised device scenarios with different resources and attack payloads. To the best of our knowledge, the proposed framework is the first in detecting compromised CPS smart grid devices with system and function-level call tracing techniques. The experimental results reveal an excellent rate for the detection of compromised devices. Specifically, performance metrics include accuracy values between 95% and 99% for the different attack scenarios. Finally, the performance analysis demonstrates that the use of the proposed framework has minimal overhead on the smart grid devices' computing resources.

Includes members:
  Member "real time analysis of privacy un aware iot applications":
    also connected through other hyperedges:
      (none)
  Member "aegis a context aware security framework for smart home systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "real time analysis of privacy un aware iot applications")
  Member "sensitive information tracking in commodity iot":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "real time analysis of privacy un aware iot applications")
  Member "detection of compromised smart grid devices with machine learning and convolution techniques":
    also connected through other hyperedges:
      (none)
  Member "advertising in the iot era vision and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "architectural software patterns for the development of iot smart applications")
  Member "iotdots a digital forensics framework for smart environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "real time analysis of privacy un aware iot applications")
  Member "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "conaml constrained adversarial machine learning for cyber physical systems")
  Member "machine learning methods for attack detection in the smart grid":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "conaml constrained adversarial machine learning for cyber physical systems")

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)