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: "vulnerabilities of electric vehicle battery packs to cyberattacks"
  Abstract: Modern vehicles are entirely controlled by electronic devices and networks that expose them to the threat of cyberattacks. Alongside, there is an enormous increase in the number of electric vehicles (EVs) within the transportation fleet. Cyber vulnerabilities are magnified with EVs due to unique risks associated with EV battery packs. The batteries provide a fixed driving range for a finite lifetime, causing the well known `range anxiety' for vehicle operators. Further, there are fire-related safety concerns with Li-ion batteries. In this study, we develop a systematic framework to assess the impact of cyberattacks on EVs and EV subsystems. We categorize this impact into physical, financial or strategic losses; each of which could be due to range, lifetime or safety of the battery packs. While the current focus of automotive cyberattacks is on short-term physical safety, we believe that it is crucial to consider long-term cyberattacks that aim to cause financial and strategic losses. Compromised charging systems such as control of the voltage regulator could lead to cyberattacks that can shorten the lifetime of a new battery pack to less than a year. Further, this also poses potential physical safety risks via the triggering of thermal (fire) events. Attacks on EV subsystems, such as auxiliary components, undoubtedly lead to the trivial effect of draining the battery, which could be up to 20% per hour and could deteriorate the performance over longer time spans. We also develop a heuristic for stealthiness to augment traditional threat models for automotive systems. The methodology and the approach presented here will help in building the foundational principles for cybersecurity in the context of electric vehicles: a nascent but critical topic in the coming years.

Connected through hyperedge HE-1 (induced by source paper "automation is no barrier to light vehicle electrification"):
  with members:
    - "a review on energy environmental and sustainability implications of connected and automated vehicles"
    - "caad computer architecture for autonomous driving"

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)