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 reliability for v2v communications around suburban and urban intersections"
  Abstract: Safe transportation is a key use-case of the 5G/LTE Rel.15+ communications, where an end-to-end reliability of 0.99999 is expected for a vehicle-to-vehicle (V2V) transmission distance of 100-200 m. Since communications reliability is related to road-safety, it is crucial to verify the fulfillment of the performance, especially for accident-prone areas such as intersections. We derive closed-form expressions for the V2V transmission reliability near suburban corners and urban intersections over finite interference regions. The analysis is based on plausible street configurations, traffic scenarios, and empirically-supported channel propagation. We show the means by which the performance metric can serve as a preliminary design tool to meet a target reliability. We then apply meta distribution concepts to provide a careful dissection of V2V communications reliability. Contrary to existing work on infinite roads, when we consider finite road segments for practical deployment, fine-grained reliability per realization exhibits bimodal behavior. Either performance for a certain vehicular traffic scenario is very reliable or extremely unreliable, but nowhere in relatively proximity to the average performance. In other words, standard SINR-based average performance metrics are analytically accurate but can be insufficient from a practical viewpoint. Investigating other safety-critical point process networks at the meta distribution-level may reveal similar discrepancies.

Connected through hyperedge HE-1 (induced by source paper "ultra reliable and low latency wireless communication tail risk and scale"):
  with members:
    - "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
    - "federated learning for ultra reliable low latency v2v communications"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "network slicing for ultra reliable low latency communication in industry 4 0 scenarios"
    - "proactive edge computing in latency constrained fog networks"
    - "introduction to ultra reliable and low latency communications in 5g"
    - "network formation in the sky unmanned aerial vehicles for multi hop wireless backhauling"

Connected through hyperedge HE-2 (induced by source paper "performance analysis of cooperative communications at road intersections using stochastic geometry tools"):
  with members:
    - "outage analysis of cooperative noma using maximum ratio combining at intersections"
    - "modeling and design of millimeter wave networks for highway vehicular communication"
    - "vehicle to vehicle communications with urban intersection path loss models"
    - "cooperative relaying under spatially and temporally correlated interference"
    - "effect of spatial interference correlation on the performance of maximum ratio combining"
    - "on the outage probability of the full duplex interference limited relay channel"
    - "dual branch mrc receivers under spatial interference correlation and nakagami fading"
    - "cooperative relaying in a poisson field of interferers a diversity order analysis"

Connected through hyperedge HE-3 (induced by source paper "fine grained vs average reliability for v2v communications around intersections"):
  with members:
    - "vehicle to vehicle communications with urban intersection path loss models"
    - "wireless network signals with moderately correlated shadowing still appear poisson"
    - "the meta distribution of the sir in poisson bipolar and cellular 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.IT(Information Theory)