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: "building transmission backbone for highway vehicular networks framework and analysis"
  Abstract: The highway vehicular ad hoc networks, where vehicles are wirelessly interconnected, rely on the multihop transmissions for end-to-end communications. This, however, is severely challenged by the unreliable wireless connections, signal attenuation, and channel contentions in the dynamic vehicular environment. To overcome the network dynamics, selecting appropriate relays for end-to-end connections is important. Different from the previous efforts (  e.g. , clustering and cooperative downloading), this paper explores the existence of stable vehicles and propose building a stable multihop transmission backbone network in the highway vehicular ad hoc network. This paper is composed of three parts. First, by analyzing the real-world vehicle traffic traces, we observe that the large-size vehicles, e.g., trucks, are typically stable with low variations of mobility and stable channel condition of low signal attenuation; this makes their interconnections stable in both connection time and transmission rate. Second, by exploring the stable vehicles, we propose a distributed protocol to build a multihop backbone link for end-to-end transmissions, accordingly forming a two-tier network architecture in highway vehicular ad hoc networks. Finally, to show the resulting data performance, we develop a queueing analysis model to evaluate the end-to-end transmission delay and throughput. Using extensive simulations, we show that the proposed transmission backbone can significantly improve the reliability of multihop data transmissions with higher throughput, less transmission interruptions, and end-to-end delay.

Connected through hyperedge HE-1 (induced by source paper "a mobility aware vehicular caching scheme in content centric networks model and optimization"):
  with members:
    - "throughput of infrastructure based cooperative vehicular networks"
    - "high reliability and low latency for vehicular networks challenges and solutions"
    - "order optimal rate of caching and coded multicasting with random demands"
    - "throughput and energy efficiency analysis of small cell networks with multi antenna base stations"
    - "femtocaching and device to device collaboration a new architecture for wireless video distribution"

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)