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: "casad can aware stealthy attack detection for in vehicle networks"
  Abstract: Nowadays, vehicles have complex in-vehicle networks (IVNs) with millions of lines of code controlling almost every function in the vehicle including safety-critical functions. It has recently been shown that IVNs are becoming increasingly vulnerable to cyber-attacks capable of taking control of vehicles, thereby threatening the safety of the passengers. Several countermeasures have been proposed in the literature in response to the arising threats, however, hurdle requirements imposed by the industry is hindering their adoption in practice. In particular, detecting attacks on IVNs is challenged by strict resource constraints and utterly complex communication patterns that vary even for vehicles of the same model. In addition, existing solutions suffer from two main drawbacks. First, they depend on the underlying vehicle configuration, and second, they are incapable of detecting certain attacks of a stealthy nature. In this paper, we propose CASAD, a CAN-Aware Stealthy-Attack Detection mechanism that does not abide by the strict specifications predefined for every vehicle model and addresses key real-world deployability challenges. Our fast, lightweight, and system-agnostic approach learns the normal behavior of IVN dynamics from historical data and detects deviations by continuously monitoring IVN traffic. We demonstrate the effectiveness of CASAD by conducting various experiments on a CAN bus prototype, a 2018 Volvo XC60, and publicly available data from two real vehicles. Our approach is experimentally shown to be effective against different attack scenarios, including the prompt detection of stealthy attacks, and has considerable potential applicability to real vehicles.

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)