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 "feasibility of 5g mm wave communication for connected autonomous vehicles"):
  Abstract: The internet-of-things (IoT) environment holds different intelligent components networked together and will enable seamless data communication between the connected components. Connected autonomous vehicles or CAVs are major components of the IoT, and the smooth, reliable, and safe operation of CAVs demands a reliable wireless communication system, which can ensure high connectivity, high throughput and low communication latency. The 5G millimeter-wave or mm-wave communication network offers such benefits, which can be the enabler of CAVs, especially for dense congested areas. In this research, we have evaluated the 5G mm-wave and Dedicated Short Range Communication (DSRC) for different CAV applications in a Network Simulator-3 (ns-3). For the CAV applications, we have evaluated the end-to-end latency, packet loss, and data rate (for both the CAV receiver and transmitter) of the 5G mm-wave. We have found that 5G mm-wave can support CAV safety applications by ensuring lower latency compared to the required minimum latency of 200 milliseconds for the forward collision warning application. For mobility applications, we have found that 5G mm-wave can support multiple CAVs with a high data receive rate, which is enough for real-time high definition video streaming for in-vehicle infotainment, with mean packet delay of 13 milliseconds. The findings from this study establish that 5G mm-wave can be the enabler of future CAVs in congested areas. Using the evaluation framework developed in this paper, public agencies can evaluate 5G mm-wave to support CAVs in congested areas, such as heavy pedestrian areas like downtown, commercial zones, under their jurisdiction.

Includes members:
  Member "end to end simulation of 5g mmwave networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "implementation of a spatial channel model for ns 3")
  Member "improved handover through dual connectivity in 5g mmwave mobile networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wireless edge computing with latency and reliability guarantees")
  Member "small scale local area and transitional millimeter wave propagation for 5g communications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "suburban fixed wireless access channel measurements and models at 28 ghz for 90 outdoor coverage")
  Member "millimeter wave vehicular communication to support massive automotive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks")
  Member "the dynamics of vehicular networks in urban environments":
    also connected through other hyperedges:
      (none)

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)