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: "context aware misbehavior detection scheme for vehicular ad hoc networks using sequential analysis of the temporal and spatial correlation of the cooperative awareness messages"
  Abstract: Vehicular ad hoc Networks (VANETs) are emerged mainly to improve road safety, traffic efficiency, and passenger comfort. The performance of most VANET applications relies on the availability of accurate and recent mobility-information, shared by neighboring vehicles. However, sharing false mobility information can disrupt any potential VANET application. Misbehavior detection is an important security component. However, existing misbehavior detection solutions lack considering the high dynamicity of vehicular context which leads to low detection accuracy and high false alarms. The use of predefined and static security thresholds are the main drawbacks of the existing solutions. In this paper, a context-aware misbehavior detection scheme (CAMDS) is proposed using sequential analysis of temporal and spatial properties of mobility information. A dynamic context reference is constructed online and timely updated using statistical techniques. Firstly, the Kalman filter algorithm is used to track the mobility information received from neighboring vehicles. Then, the innovation errors of the Kalman filter are utilized to construct a temporal consistency assessment model for each vehicle using Box-plot. Then, the Hampel filter is used to construct a spatial consistency assessment model that represents the context reference model. Similarly, plausibility assessment reference models are built online and timely updated using the Hampel filter and by utilizing the consistency assessment reference model of neighboring information. Finally, a message is classified as suspicious if its consistency and plausibility scores deviate much from the context reference model. The proposed context-aware scheme achieved a 73% reduction in false Alarm rate while it achieves a 37% improvement in the detection rate. This proves the effectiveness of the proposed scheme compared with the existing static solutions.

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.CY(Computers and Society)