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: "enhancement of low cost gnss localization in connected vehicle networks using rao blackwellized particle filters"
  Abstract: An essential function for automated vehicle technologies is accurate localization. It is difficult, however, to achieve lane-level accuracy with low-cost Global Navigation Satellite System (GNSS) receivers due to the biased noisy pseudo-range measurements. Approaches such as Differential GNSS can improve the accuracy, but usually require an enormous amount of investment in base stations. The emerging connected vehicle technologies provide an alternative approach to improving the localization accuracy. It has been shown in this paper that localization accuracy can be enhanced by fusing GNSS information within a group of connected vehicles and matching the configuration of the group to a digital map to eliminate the common bias in localization. A Rao-Blackwellized particle filter (RBPF) was used to jointly estimate the common biases of the pseudo-ranges and the vehicles positions. Multipath biases, which are non-common to vehicles, were mitigated by a multi-hypothesis detection-rejection approach. The temporal correlation was exploited through the prediction-update process. The proposed approach was compared to the existing static and smoothed static methods in the intersection scenario. Simulation results show that the proposed algorithm reduced the estimation error by fifty percent and reduced the estimation variance by two orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "improving localization accuracy in connected vehicle networks using rao blackwellized particle filters theory simulations and experiments"):
  with members:
    - "empirical study of dsrc performance based on safety pilot model deployment data"
    - "the impact of road configuration on v2v based cooperative localization"
    - "rao blackwellised particle filtering for dynamic bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "optimization of vehicle connections in v2v based cooperative localization"):
  with members:
    - "the impact of road configuration on v2v based cooperative localization"
    - "improving localization accuracy in connected vehicle networks using rao blackwellized particle filters theory simulations and experiments"

Connected through hyperedge HE-3 (induced by source paper "the impact of road configuration in v2v based cooperative localization mathematical analysis and real world evaluation"):
  with members:
    - "the impact of road configuration on v2v based cooperative localization"
    - "improving localization accuracy in connected vehicle networks using rao blackwellized particle filters theory simulations and experiments"

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

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.RO(Robotics)