human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "delay and doppler spreads of nonstationary vehicular channels for safety relevant scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toward intelligent vehicular networks a machine learning framework")
  Member "geometry based vehicle to vehicle channel modeling for large scale simulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "estimating the relative speed of rf jammers in vanets")

Question: Please tell me which class the source paper 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)