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: "d2d based v2v communications with latency and reliability constraints"
  Abstract: Direct device-to-device (D2D) communication has been proposed as a possible enabler for vehicle-to-vehicle (V2V) applications, where the incurred intra-cell interference and the stringent latency and reliability requirements are challenging issues. In this paper, we investigate the radio resource management problem for D2D-based V2V communications. Firstly, we analyze and mathematically model the actual requirements for vehicular communications and traditional cellular links. Secondly, we propose a problem formulation to fulfill these requirements, and then a Separate Resource Block allocation and Power control (SRBP) algorithm to solve this problem. Finally, simulations are presented to illustrate the improved performance of the proposed SRBP scheme compared to some other existing methods.

Connected through hyperedge HE-1 (induced by source paper "distributed federated learning for ultra reliable low latency vehicular communications"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "joint power and resource allocation of d2d communication with low resolution adc"):
  with members:
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "pilot reuse among d2d users in d2d underlaid massive mimo systems"
    - "energy efficiency and sum rate when massive mimo meets device to device communication"
    - "uplink achievable rate for massive mimo systems with low resolution adc"
    - "the interplay between massive mimo and underlaid d2d networking"

Connected through hyperedge HE-3 (induced by source paper "ultra reliable and low latency vehicular communication using optical camera communications"):
  with members:
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "ultra reliable low latency vehicle to infrastructure wireless communications with edge computing"
    - "coverage and rate of downlink sequence transmissions with reliability guarantees"

Connected through hyperedge HE-4 (induced by source paper "stochastic geometry modeling of cellular v2x communication over shared channels"):
  with members:
    - "success probability and area spectral efficiency of a vanet modeled as a cox process"
    - "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"
    - "asymptotic blind spot analysis of localization networks under correlated blocking using a poisson line process"
    - "a primer on cellular network analysis using stochastic geometry"
    - "artificial noise aided secure transmission in large scale spectrum sharing networks"
    - "physical layer security in three tier wireless sensor networks a stochastic geometry approach"
    - "on stochastic geometry modeling of cellular uplink transmission with truncated channel inversion power control"

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)