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: "moments of interference in vehicular networks with hardcore headway distance"
  Abstract: Interference statistics in vehicular networks have long been studied using the Poisson Point Process (PPP) for the locations of vehicles. In roads with few number of lanes and restricted overtaking, this model becomes unrealistic because it assumes that the vehicles can come arbitrarily close to each other. In this paper, we model the headway distance (the distance between the head of a vehicle and the head of its follower) equal to the sum of a constant hardcore distance and an exponentially distributed random variable. We study the mean, the variance and the skewness of interference at the origin with this deployment model. Even though the pair correlation function becomes complicated, we devise simple formulae to capture the impact of hardcore distance on the variance of interference in comparison with a PPP model of equal intensity. In addition, we study the extreme scenario where the interference originates from a lattice. We show how to relate the variance of interference due to a lattice to that of a PPP under Rayleigh fading.

Connected through hyperedge HE-1 (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"
    - "performance of a link in a field of vehicular interferers with hardcore headway distance"
    - "an analytical framework for coverage in cellular networks leveraging vehicles"
    - "fine grained vs average reliability for v2v communications around intersections"
    - "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-2 (induced by source paper "temporal correlation of interference in vehicular networks with shifted exponential time headways"):
  with members:
    - "temporal correlation of interference and outage in mobile networks over one dimensional finite regions"
    - "diversity polynomials for the analysis of temporal correlations in wireless networks"
    - "analytical modeling of uplink cellular networks"
    - "spatial and temporal correlation of the interference in aloha ad hoc 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)