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: "tvr tall vehicle relayingin vehicular networks"
  Abstract: Vehicle-to-Vehicle (V2V) communication is a core technology for enabling safety and non-safety applications in next generation intelligent transportation systems. Due to relatively low heights of the antennas, V2V communication is often influenced by topographic features, man-made structures, and other vehicles located between the communicating vehicles. On highways, it was shown experimentally that vehicles can obstruct the line of sight (LOS) communication up to 50 percent of the time; furthermore, a single obstructing vehicle can reduce the power at the receiver by more than 20 dB. Based on both experimental measurements and simulations performed using a validated channel model, we show that the elevated position of antennas on tall vehicles improves communication performance. Tall vehicles can significantly increase the effective communication range, with an improvement of up to 50 percent in certain scenarios. Using these findings, we propose a new V2V relaying scheme called tall vehicle relaying (TVR) that takes advantage of better channel characteristics provided by tall vehicles. TVR distinguishes between tall and short vehicles and, where appropriate, chooses tall vehicles as next hop relays. We investigate TVR’s system-level performance through a combination of link-level experiments and system-level simulations and show that it outperforms existing techniques.

Connected through hyperedge HE-1 (induced by source paper "a measurement based multilink shadowing model for v2v network simulations of highway scenarios"):
  with members:
    - "a measurement based shadow fading model for vehicle to vehicle network simulations"
    - "em based channel estimation from crowd sourced rssi samples corrupted by noise and interference"
    - "statistical modeling and estimation of censored pathloss data"
    - "geometry based vehicle to vehicle channel modeling for large scale simulation"

Connected through hyperedge HE-2 (induced by source paper "exploring the practical limits of cooperative awareness in vehicular communications"):
  with members:
    - "ecpr environment and context aware combined power and rate distributed congestion control for vehicular communications"
    - "a measurement based shadow fading model for vehicle to vehicle network simulations"
    - "geometry based vehicle to vehicle channel modeling for large scale simulation"

Connected through hyperedge HE-3 (induced by source paper "modeling the evolution of line of sight blockage for v2v channels"):
  with members:
    - "a measurement based shadow fading model for vehicle to vehicle network simulations"
    - "vehicular communications survey and challenges of channel and propagation models"
    - "geometry based vehicle to vehicle channel modeling for large scale simulation"

Connected through hyperedge HE-4 (induced by source paper "vehicular communications survey and challenges of channel and propagation models"):
  with members:
    - "a measurement based shadow fading model for vehicle to vehicle network simulations"
    - "delay and doppler spreads of nonstationary vehicular channels for safety relevant scenarios"
    - "geometry based vehicle to vehicle channel modeling for large scale simulation"

Connected through hyperedge HE-5 (induced by source paper "geometry based vehicle to vehicle channel modeling for large scale simulation"):
  with members:
    - "a measurement based shadow fading model for vehicle to vehicle network simulations"
    - "delay and doppler spreads of nonstationary vehicular channels for safety relevant scenarios"

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)