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: "viden attacker identification on in vehicle networks"
  Abstract: Various defense schemes --- which determine the presence of an attack on the in-vehicle network --- have recently been proposed. However, they fail to identify which Electronic Control Unit (ECU) actually mounted the attack. Clearly, pinpointing the attacker ECU is essential for fast/efficient forensic, isolation, security patch, etc. To meet this need, we propose a novel scheme, called Viden (Voltage-based attacker identification), which can identify the attacker ECU by measuring and utilizing voltages on the in-vehicle network. The first phase of Viden, called ACK learning, determines whether or not the measured voltage signals really originate from the genuine message transmitter. Viden then exploits the voltage measurements to construct and update the transmitter ECUs' voltage profiles as their fingerprints. It finally uses the voltage profiles to identify the attacker ECU. Since Viden adapts its profiles to changes inside/outside of the vehicle, it can pinpoint the attacker ECU under various conditions. Moreover, its efficiency and design-compliance with modern in-vehicle network implementations make Viden practical and easily deployable. Our extensive experimental evaluations on both a CAN bus prototype and two real vehicles have shown that Viden can accurately fingerprint ECUs based solely on voltage measurements and thus identify the attacker ECU with a low false identification rate of 0.2%.

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"
    - "cloaking the clock emulating clock skew in controller area networks"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-2 (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"
    - "identifying ecus using inimitable characteristics of signals in controller area networks"

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"
    - "cloaking the clock emulating clock skew in controller area 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"
    - "cloaking the clock emulating clock skew in controller area networks"

Connected through hyperedge HE-5 (induced by source paper "on the robustness of signal characteristic based sender identification"):
  with members:
    - "identifying ecus using inimitable characteristics of signals in controller area networks"

Connected through hyperedge HE-6 (induced by source paper "casad can aware stealthy attack detection for in vehicle networks"):
  with members:
    - "identifying ecus using inimitable characteristics of signals in controller area networks"

Connected through hyperedge HE-7 (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"
    - "cloaking the clock emulating clock skew in controller area networks"
    - "identifying ecus using inimitable characteristics of signals in controller area networks"

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"
    - "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)