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: "reception probabilities in 5g vehicular communications close to intersections"
  Abstract: Vehicular networks allow vehicles to constantly share information with each other and their surrounding, and are expected to be an integral part in future intelligent transportation system (ITS). However, the diversity of ITS applications in combination with the extremely stringent demands in particular posed by safety critical applications makes the design of vehicular communication a challenging task. 5G device-to-device communication as well as 802.11p are promising to address this challenge. In order to guide and validate the design process, analytical expressions of key performance metrics such as outage probability an throughput are necessary. In this paper, we focus on the intersection scenario, and present a general procedure to analytically determine the success probability of a selected link as well as system-wide throughput. We provide an overview of the salient properties of vehicular communication systems near intersections and show how the procedure can be used to model signal propagation conditions typical to different environments of practical relevance, for instance rural and urban scenarios. The results indicate that the procedure is sufficiently general and flexible to deal with a variety of scenarios, and can thus serve as a useful design tool for communication system engineers, complementing simulations and experiments.

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.SY(Systems and Control)