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: "empirical study of dsrc performance based on safety pilot model deployment data"
  Abstract: Dedicated short range communication (DSRC) was designed to provide reliable wireless communication for intelligent transportation system applications. Sharing information among cars and between cars and the infrastructure, pedestrians, or “the cloud” has great potential to improve safety, mobility, and fuel economy. DSRC is being considered by the U.S. Department of Transportation to be required for ground vehicles. In the past, their performance has been assessed thoroughly in the laboratories and limited field testing, but not on a large fleet. In this paper, we present the analysis of DSRC performance using data from the world’s largest connected vehicle test program—Safety Pilot Model Deployment lead by the University of Michigan. We first investigate their maximum and effective range, and then study the effect of environmental factors, such as trees/foliage, weather, buildings, vehicle travel direction, and road elevation. The results can be used to guide future DSRC equipment placement and installation, and can be used to develop DSRC communication models for numerical simulations.

Connected through hyperedge HE-1 (induced by source paper "mcity data collection for automated vehicles study"):
  with members:
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "an optimal lidar configuration approach for self driving cars"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "developing robot driver etiquette based on naturalistic human driving behavior"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "pseudo lidar from visual depth estimation bridging the gap in 3d object detection for autonomous driving"
    - "orthographic feature transform for monocular 3d object detection"

Connected through hyperedge HE-2 (induced by source paper "cluster naturalistic driving encounters using deep unsupervised learning"):
  with members:
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "autoencoder regularized network for driving style representation learning"
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
    - "trafficnet an open naturalistic driving scenario library"
    - "game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems"
    - "accelerated evaluation of automated vehicles in car following maneuvers"

Connected through hyperedge HE-3 (induced by source paper "extracting v2v encountering scenarios from naturalistic driving database"):
  with members:
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "accelerated evaluation of automated vehicles in car following maneuvers"

Connected through hyperedge HE-4 (induced by source paper "improving localization accuracy in connected vehicle networks using rao blackwellized particle filters theory simulations and experiments"):
  with members:
    - "the impact of road configuration on v2v based cooperative localization"
    - "enhancement of low cost gnss localization in connected vehicle networks using rao blackwellized particle filters"
    - "rao blackwellised particle filtering for dynamic bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "enhancement of low cost gnss localization in connected vehicle networks using rao blackwellized particle filters"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian 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)