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: "distributed and fair beaconing rate adaptation for congestion control in vehicular networks"
  Abstract: Cooperative inter-vehicular applications rely on the exchange of broadcast single-hop status messages among vehicles, called beacons. The aggregated load on the wireless channel due to periodic beacons can prevent the transmission of other types of messages, what is called channel congestion due to beaconing activity. In this paper, we approach the problem of controlling the beaconing rate on each vehicle by modeling it as a Network Utility Maximization (NUM) problem. This allows us to formally apply the notion of fairness of a beaconing rate allocation in vehicular networks and to control the trade-off between efficiency and fairness. The NUM methodology provides a rigorous framework to design a broad family of simple and decentralized algorithms, with proved convergence guarantees to a  fair  allocation solution. In this context, we focus exclusively in beaconing rate control and propose the Fair Adaptive Beaconing Rate for Intervehicular Communications (FABRIC) algorithm, which uses a particular scaled gradient projection algorithm to solve the dual of the NUM problem. The desired fairness notion in the allocation can be established with an algorithm parameter. Simulation results validate our approach and show that FABRIC converges to fair rate allocations in multi-hop and dynamic scenarios.

Connected through hyperedge HE-1 (induced by source paper "network utility maximization based on incentive mechanism for truthful reporting of local information"):
  with members:
    - "applications of economic and pricing models for wireless network security a survey"
    - "joint scheduling and transmission power control in wireless ad hoc networks"
    - "energy aware traffic offloading for green heterogeneous networks"
    - "propagation path loss models for 5g urban micro and macro cellular scenarios"
    - "context aware small cell networks how social metrics improve wireless resource allocation"
    - "distributed pricing based user association for downlink heterogeneous cellular networks"
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "energy efficiency and spectral efficiency tradeoff in device to device d2d communications"

Connected through hyperedge HE-2 (induced by source paper "ecpr environment and context aware combined power and rate distributed congestion control for vehicular communications"):
  with members:
    - "evaluation of synchronous and asynchronous reactive distributed congestion control algorithms for the its g5 vehicular systems"
    - "a measurement based shadow fading model for vehicle to vehicle network simulations"
    - "vehicular communications survey and challenges of channel and propagation models"
    - "distributed and fair beaconing congestion control schemes for vehicular networks"
    - "geometry based vehicle to vehicle channel modeling for large scale simulation"

Connected through hyperedge HE-3 (induced by source paper "distributed rate and power control in vehicular networks"):
  with members:
    - "ecpr environment and context aware combined power and rate distributed congestion control for vehicular communications"
    - "distributed and fair beaconing congestion control schemes for vehicular networks"

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)