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: "wireless vehicular networks in emergencies a single frequency network approach"
  Abstract: Obtaining high quality sensor information is critical in vehicular emergencies. However, existing standards such as IEEE 802.11p/DSRC and LTE-A cannot support either the required data rates or the latency requirements. One solution to this problem is for municipalities to invest in dedicated base stations to ensure that drivers have the information they need to make safe decisions in or near accidents. In this paper we further propose that these municipality-owned base stations form a Single Frequency Network (SFN). In order to ensure that transmissions are reliable, we derive tight bounds on the outage probability when the SFN is overlaid on an existing cellular network. Using our bounds, we propose a transmission power allocation algorithm. We show that our power allocation model can reduce the total instantaneous SFN transmission power up to $20$ times compared to a static uniform power allocation solution, for the considered scenarios. The result is particularly important when base stations rely on an off-grid power source (i.e., batteries).

Connected through hyperedge HE-1 (induced by source paper "coverage and rate analysis of downlink cellular vehicle to everything c v2x communication"):
  with members:
    - "stochastic geometry modeling of cellular v2x communication over shared channels"
    - "how to dimension 5g network when users are distributed on roads modeled by poisson line process"
    - "poisson cox point processes for vehicular networks"
    - "success probability and area spectral efficiency of a vanet modeled as a cox process"
    - "characterizing shortest paths in road systems modeled as manhattan poisson line processes"
    - "an analytical framework for coverage in cellular networks leveraging vehicles"
    - "coverage analysis of a vehicular network modeled as cox process driven by poisson line process"
    - "modeling and design of millimeter wave networks for highway vehicular communication"

Connected through hyperedge HE-2 (induced by source paper "multi radio 5g architecture for connected and autonomous vehicles application and design insights"):
  with members:
    - "beam alignment for millimetre wave links with motion prediction of autonomous vehicles"
    - "decoding delay performance of random linear network coding for broadcast"
    - "agile calibration process of full stack simulation frameworks for v2x communications"
    - "modeling and design of millimeter wave networks for highway vehicular communication"
    - "mmwave system for future its a mac layer approach for v2x beam steering"
    - "rethinking the intercept probability of random linear network coding"

Connected through hyperedge HE-3 (induced by source paper "modeling and design of millimeter wave networks for highway vehicular communication"):
  with members:
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "analysis and optimization of sparse random linear network coding for reliable multicast services"
    - "stochastic geometry modeling and analysis of multi tier millimeter wave cellular networks"
    - "millimeter wave distance dependent large scale propagation measurements and path loss models for outdoor and indoor 5g systems"
    - "millimeter wave cellular networks a mac layer perspective"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "what will 5g be"

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.IT(Information Theory)