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: "on the robustness of signal characteristic based sender identification"
  Abstract: Vehicles become more vulnerable to remote attackers in modern days due to their increasing connectivity and range of functionality. Such increased attack vectors enable adversaries to access a vehicle Electronic Control Unit (ECU). As of today in-vehicle access can cause drastic consequences, because the most commonly used in-vehicle bus technology, the Controller Area Network (CAN), lacks sender identification. With low limits on bandwidth and payload, as well as resource constrains on hardware, usage of cryptographic measures is limited. As an alternative, sender identification methods were presented, identifying the sending ECU on the basis of its analog message signal. While prior works showed promising results on the security and feasibility for those approaches, the potential changes in signals over a vehicle's lifetime have only been partly addressed. This paper closes this gap. We conduct a 4~months measurement campaign containing more than 80,000 frames from a real vehicle. The data reflects different driving situations, different seasons and weather conditions, a 19-week break, and a car repair altering the physical CAN properties. We demonstrate the impact of temperature dependencies, analyze the signal changes and define strategies for their handling. In the evaluation, the identification rate can be increased from 91.23% to 99.98% by a targeted updating of the system parameters. At the same time, the detection of intrusions can be improved from 76.83% to 99.74%, while no false positives occured during evaluation. Lastly, we show how to increase the overall performance of such systems by double monitoring the bus at different positions.

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)