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: "identifying ecus using inimitable characteristics of signals in controller area networks"
  Abstract: In the last several decades, the automotive industry has come to incorporate the latest Information and Communications (ICT) technology, increasingly replacing mechanical components of vehicles with electronic components. These electronic control units (ECUs) communicate with each other in an in-vehicle network that makes the vehicle both safer and easier to drive. Controller Area Networks (CANs) are the current standard for such high quality in-vehicle communication. Unfortunately, however, CANs do not currently offer protection against security attacks. In particular, they do not allow for message authentication and hence are open to attacks that replay ECU messages for malicious purposes. Applying the classic cryptographic method of message authentication code (MAC) is not feasible since the CAN data frame is not long enough to include a sufficiently long MAC to provide effective authentication. In this paper, we propose a novel identification method, which works in the physical layer of an in-vehicle CAN network. Our method identifies ECUs using inimitable characteristics of signals enabling detection of a compromised or alien ECU being used in a replay attack. Unlike previous attempts to address security issues in the in-vehicle CAN network, our method works by simply adding a monitoring unit to the existing network, making it deployable in current systems and compliant with required CAN standards. Our experimental results show that the bit string and classification algorithm that we utilized yielded more accurate identification of compromised ECUs than any other method proposed to date. The false positive rate is more than 2 times lower than the method proposed by P.-S. Murvay et al. This paper is also the first to identify potential attack models that systems should be able to detect.

Connected through hyperedge HE-1 (induced by source paper "network intrusion detection systems for in vehicle network technical report"):
  with members:
    - "an entropy analysis based intrusion detection system for controller area network in vehicles"
    - "viden attacker identification on in vehicle networks"

Connected through hyperedge HE-2 (induced by source paper "on the robustness of signal characteristic based sender identification"):
  with members:
    - "viden attacker identification on in vehicle networks"

Connected through hyperedge HE-3 (induced by source paper "casad can aware stealthy attack detection for in vehicle networks"):
  with members:
    - "viden attacker identification on in vehicle networks"

Connected through hyperedge HE-4 (induced by source paper "a survey of intrusion detection systems leveraging host data"):
  with members:
    - "setting the threshold for high throughput detectors a mathematical approach for ensembles of dynamic heterogeneous probabilistic anomaly detectors"
    - "malware detection on general purpose computers using power consumption monitoring a proof of concept and case study"
    - "automated behavioral analysis of malware a case study of wannacry ransomware"
    - "graphprints towards a graph analytic method for network anomaly detection"

Connected through hyperedge HE-5 (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"
    - "cloaking the clock emulating clock skew in controller area networks"

Connected through hyperedge HE-6 (induced by source paper "towards a can ids based on a neural network data field predictor"):
  with members:
    - "exploiting the shape of can data for in vehicle intrusion detection"
    - "actt automotive can tokenization and translation"

Connected through hyperedge HE-7 (induced by source paper "actt automotive can tokenization and translation"):
  with members:
    - "exploiting the shape of can data for in vehicle intrusion detection"
    - "using data analytics to detect anomalous states in vehicles"

Connected through hyperedge HE-8 (induced by source paper "vehicle security risk assessment in transportation"):
  with members:
    - "inferring transportation modes from gps trajectories using a convolutional neural network"
    - "viden attacker identification on in vehicle 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)