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 "ecpr environment and context aware combined power and rate distributed congestion control for vehicular communications"):
  Abstract: Abstract   Safety and efficiency applications in vehicular networks rely on the exchange of periodic messages between vehicles. These messages contain position, speed, heading, and other vital information that makes the vehicles aware of their surroundings. The drawback of exchanging periodic cooperative messages is that they generate significant channel load. Decentralized Congestion Control (DCC) algorithms have been proposed to minimize the channel load. However, while the rationale for periodic message exchange is  to improve awareness , existing DCC algorithms do not use awareness as a metric for deciding when, at what power, and at what rate the periodic messages need to be sent in order to make sure all vehicles are informed. We propose an environment- and context-aware DCC algorithm combines power and rate control in order to improve cooperative awareness by adapting to both specific propagation environments ( e.g. , urban intersections, open highways, suburban roads) as well as application requirements ( e.g. , different target cooperative awareness range). Studying various operational conditions ( e.g. , speed, direction, and application requirement), ECPR adjusts the transmit power of the messages in order to reach the desired awareness ratio at the target distance while at the same time controlling the channel load using an adaptive rate control algorithm. By performing extensive simulations, including realistic propagation as well as environment modeling and realistic vehicle operational environments (varying demand on both awareness range and rate), we show that ECPR can increase awareness by 20% while keeping the channel load and interference at almost the same level. When permitted by the awareness requirements, ECPR can improve the average message rate by 18% compared to algorithms that perform rate adaptation only.

Includes members:
  Member "distributed and fair beaconing rate adaptation for congestion control in vehicular networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "network utility maximization based on incentive mechanism for truthful reporting of local information")
  Member "evaluation of synchronous and asynchronous reactive distributed congestion control algorithms for the its g5 vehicular systems":
    also connected through other hyperedges:
      (none)
  Member "a measurement based shadow fading model for vehicle to vehicle network simulations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a measurement based multilink shadowing model for v2v network simulations of highway scenarios")
  Member "vehicular communications survey and challenges of channel and propagation models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non orthogonal multiple access with orthogonal time frequency space signal transmission")
  Member "distributed and fair beaconing congestion control schemes for vehicular networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed rate and power control in vehicular networks")
  Member "geometry based vehicle to vehicle channel modeling for large scale simulation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "estimating the relative speed of rf jammers in vanets")

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.OH(Other Computer Science)