human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "cloaking the clock emulating clock skew in controller area networks"
  Abstract: Automobiles are equipped with Electronic Control Units (ECU) that communicate via in-vehicle network protocol standards such as Controller Area Network (CAN). These protocols are designed under the assumption that separating in-vehicle communications from external networks is sufficient for protection against cyber attacks. This assumption, however, has been shown to be invalid by recent attacks in which adversaries were able to infiltrate the in-vehicle network. Motivated by these attacks, intrusion detection systems (IDSs) have been proposed for in-vehicle networks that attempt to detect attacks by making use of device fingerprinting using properties such as clock skew of an ECU. In this paper, we propose the cloaking attack, an intelligent masquerade attack in which an adversary modifies the timing of transmitted messages in order to match the clock skew of a targeted ECU. The attack leverages the fact that, while the clock skew is a physical property of each ECU that cannot be changed by the adversary, the estimation of the clock skew by other ECUs is based on network traffic, which, being a cyber component only, can be modified by an adversary. We implement the proposed cloaking attack and test it on two IDSs, namely, the current state-of-the-art IDS and a new IDS that we develop based on the widely-used Network Time Protocol (NTP). We implement the cloaking attack on two hardware testbeds, a prototype and a real connected vehicle, and show that it can always deceive both IDSs. We also introduce a new metric called the Maximum Slackness Index to quantify the effectiveness of the cloaking attack even when the adversary is unable to precisely match the clock skew of the targeted ECU.

Connected through hyperedge HE-1 (induced by source paper "tacan transmitter authentication through covert channels in controller area networks"):
  with members:
    - "mitigating vulnerabilities of voltage based intrusion detection systems in controller area networks"
    - "shape of the cloak formal analysis of clock skew based intrusion detection system in controller area networks"
    - "viden attacker identification on in vehicle networks"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-2 (induced by source paper "covert channel based transmitter authentication in controller area networks"):
  with members:
    - "tacan transmitter authentication through covert channels in controller area networks"
    - "shape of the cloak formal analysis of clock skew based intrusion detection system in controller area networks"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-3 (induced by source paper "canto covert authentication with timing channels over optimized traffic flows for can"):
  with members:
    - "tacan transmitter authentication through covert channels in controller area networks"
    - "viden attacker identification on in vehicle networks"

Connected through hyperedge HE-4 (induced by source paper "mitigating vulnerabilities of voltage based intrusion detection systems in controller area networks"):
  with members:
    - "shape of the cloak formal analysis of clock skew based intrusion detection system in controller area networks"
    - "viden attacker identification on in vehicle networks"

Connected through hyperedge HE-5 (induced by source paper "leap a lightweight encryption and authentication protocol for in vehicle communications"):
  with members:
    - "an entropy analysis based intrusion detection system for controller area network in vehicles"

Connected through hyperedge HE-6 (induced by source paper "shape of the cloak formal analysis of clock skew based intrusion detection system in controller area networks"):
  with members:
    - "tacan transmitter authentication through covert channels in controller area networks"
    - "viden attacker identification on in vehicle networks"
    - "identifying ecus using inimitable characteristics of signals in controller area networks"

Question: Please tell me which class the center node 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)