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: "exploring the practical limits of cooperative awareness in vehicular communications"
  Abstract: We perform an extensive study of cooperative awareness in vehicular networks based on periodic message exchange. We start by analyzing measurements collected on four test sites across Europe. To measure cooperative awareness, we use three metrics: 1) neighborhood awareness ratio, 2) ratio of neighbors above range, and 3) packet delivery rate. Using the collected data, we define a simple model for calculating neighborhood awareness given a packet delivery ratio for an environment. Finally, we perform realistic large-scale simulations to explore the achievable performance of cooperative awareness under realistic transmit power and transmit rate constraints. Our measurements and simulation results show the following: 1) Above a certain threshold, there is little benefit in increasing the cooperative message rate to improve awareness; higher transmit power and fewer message transmissions are a better approach, since message delivery is dominated by shadowing; 2) The efficacy of cooperative awareness varies greatly in different environments on both large scale (e.g., 90% awareness is achievable up to 200 m in urban compared to 500 m in highway) and small scale (e.g., vehicles in nearby streets can have significantly different awareness levels); 3) Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications have distinct awareness patterns; 4) Each location has distinct transmit power that achieves high awareness; 5) Achieving high awareness levels results in increased reception of potentially unwanted messages; therefore, a balance needs to be found between awareness and interference, depending on the specific context. We hope that our results will serve as a starting point for designing more effective periodic message exchange services for cooperative awareness.

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.NI(Networking and Internet Architecture)