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: "simulation and measurement based vehicle to vehicle channel characterization accuracy and constraint analysis"
  Abstract: In this paper, a deterministic channel model for vehicle-to-vehicle (V2V) communication is compared against channel measurement results collected during a V2V channel measurement campaign using a channel sounder. Channel metrics such as channel gain, delay and Doppler spreads, eigenvalue (EV) distribution, and antenna correlations are derived from the ray-tracing (RT) simulations as well as from the measurement data obtained from two different measurements in an urban four-way intersection. The channel metrics are compared separately for line-of-sight (LOS) and non-LOS (NLOS) situations. Most power contributions arise from the LOS component (if present) as well as from multipaths with single bounce reflections. Measurement and simulation results of the received power show a very good agreement in the presence of LOS, as most of the received power is carried by the LOS component. In NLOS, the difference is large because the ray-tracer is unable to capture some of the channel characteristics due to the underlying limitations of our ray-based propagation model. Despite the limitations, the model is suitable to characterize some, but not all, of the channel properties in a sufficient manner. We find that the diffuse scattering and multibounced nonspecular reflections must be considered for an accurate prediction of the channel in such a rich scattering environment.

Connected through hyperedge HE-1 (induced by source paper "scheduling and power control for v2v broadcast communications with co channel and adjacent channel interference"):
  with members:
    - "temporal correlation of interference in vehicular networks with shifted exponential time headways"
    - "a measurement based shadow fading model for vehicle to vehicle network simulations"

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)