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: "improving localization accuracy in connected vehicle networks using rao blackwellized particle filters theory simulations and experiments"
  Abstract: A crucial function for automated vehicle technologies is accurate localization. Lane-level accuracy is not readily available from low-cost Global Navigation Satellite System (GNSS) receivers because of factors such as multipath error and atmospheric bias. Approaches such as Differential GNSS can improve localization accuracy, but usually require investment in expensive base stations. Connected vehicle technologies provide an alternative approach to improving the localization accuracy. It will be shown in this paper that localization accuracy can be enhanced using crude GNSS measurements from a group of connected vehicles, by matching their locations to a digital map. A Rao-Blackwellized particle filter (RBPF) is used to jointly estimate the common biases of the pseudo-ranges and the vehicle positions. Multipath biases, which introduce receiver-specific (non-common) error, are mitigated by a multi-hypothesis detection-rejection approach. The temporal correlation of the estimations is exploited through the prediction-update process. The proposed approach is compared to existing methods using both simulations and experimental results. It was found that the proposed algorithm can eliminate the common biases and reduce the localization error to below 1 meter under open sky conditions.

Connected through hyperedge HE-1 (induced by source paper "interpenetrating cooperative localization in dynamic connected vehicle networks"):
  with members:
    - "optimization of vehicle connections in v2v based cooperative localization"
    - "the impact of road configuration in v2v based cooperative localization mathematical analysis and real world evaluation"

Connected through hyperedge HE-2 (induced by source paper "semi interpenetrating cooperative localization in connected vehicle networks"):
  with members:
    - "optimization of vehicle connections in v2v based cooperative localization"
    - "the impact of road configuration in v2v based cooperative localization mathematical analysis and real world evaluation"

Connected through hyperedge HE-3 (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"
    - "enhancement of low cost gnss localization in connected vehicle networks using rao blackwellized particle filters"

Connected through hyperedge HE-4 (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"
    - "enhancement of low cost gnss localization in connected vehicle networks using rao blackwellized particle filters"

Connected through hyperedge HE-5 (induced by source paper "the impact of road configuration on v2v based cooperative localization"):
  with members:
    - "enhancement of low cost gnss localization in connected vehicle networks using rao blackwellized particle filters"

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.SY(Systems and Control)