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: "fine grained vs average reliability for v2v communications around intersections"
  Abstract: Intersections are critical areas of the transportation infrastructure associated with 47% of all road accidents. Vehicle-to-vehicle (V2V) communication has the potential of preventing up to 35% of such serious road collisions. In fact, under the 5G/LTE Rel.15+ standardization, V2V is a critical use-case not only for the purpose of enhancing road safety, but also for enabling traffic efficiency in modern smart cities. Under this anticipated 5G definition, high reliability of 0.99999 is expected for semi-autonomous vehicles (i.e., driver-in-the-loop). As a consequence, there is a need to assess the reliability, especially for accident-prone areas, such as intersections. We unpack traditional average V2V reliability in order to quantify its related fine-grained V2V reliability. Contrary to existing work on infinitely large roads, when we consider finite road segments of significance to practical real-world deployment, fine-grained reliability exhibits bimodal behavior. Performance for a certain vehicular traffic scenario is either very reliable or extremely unreliable, but nowhere in relative proximity to the average performance.

Connected through hyperedge HE-1 (induced by source paper "optimized age of information tail for ultra reliable low latency communications in vehicular networks"):
  with members:
    - "a general formula for the stationary distribution of the age of information and its application to single server queues"
    - "optimal control of status updates in a multiple access channel with stability constraints"
    - "average peak age of information minimization in uav assisted iot networks"
    - "saddlepoint approximations for rayleigh block fading channels"
    - "optimal status updating with a finite battery energy harvesting source"
    - "statistical guarantee optimization for aoi in single hop and two hop systems with periodic arrivals"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "dynamic task offloading and resource allocation for ultra reliable low latency edge computing"

Connected through hyperedge HE-2 (induced by source paper "ultra reliable and low latency vehicular communication an active learning approach"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "ultra reliable low latency vehicular networks taming the age of information tail"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "a statistical learning approach to ultra reliable low latency communication"
    - "joint information freshness and completion time optimization for vehicular networks"
    - "gaussian processes for nonlinear signal processing an overview of recent advances"

Connected through hyperedge HE-3 (induced by source paper "the meta distribution of the sir in linear motorway vanets"):
  with members:
    - "outage in motorway multi lane vanets with hardcore headway distance using synthetic traces"
    - "moments of interference in vehicular networks with hardcore headway distance"
    - "performance of a link in a field of vehicular interferers with hardcore headway distance"
    - "an analytical framework for coverage in cellular networks leveraging vehicles"
    - "the meta distribution of the sir for cellular networks with power control"
    - "coverage analysis of a vehicular network modeled as cox process driven by poisson line process"
    - "the meta distribution of the sir in poisson bipolar and cellular networks"
    - "stochastic geometry modeling of cellular networks analysis simulation and experimental validation"

Connected through hyperedge HE-4 (induced by source paper "ultra reliable low latency vehicular networks taming the age of information tail"):
  with members:
    - "average peak age of information minimization in uav assisted iot networks"
    - "joint communication and control for wireless autonomous vehicular platoon systems"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "millimeter wave v2v communications distributed association and beam alignment"
    - "towards low latency and ultra reliable vehicle to vehicle communication"
    - "latency and reliability aware task offloading and resource allocation for mobile edge computing"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"

Connected through hyperedge HE-5 (induced by source paper "ultra reliable and low latency vehicular transmission an extreme value theory approach"):
  with members:
    - "joint communication and control for wireless autonomous vehicular platoon systems"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "millimeter wave v2v communications distributed association and beam alignment"
    - "towards low latency and ultra reliable vehicle to vehicle communication"
    - "a tutorial on spectral clustering"

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)