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: "fusing loop and gps probe measurements to estimate freeway density"
  Abstract: In an age of ever-increasing penetration of Global Positioning System-enabled mobile devices, the potential of real-time “probe” location information for estimating the state of transportation networks is receiving increasing attention. Much work has been done on using probe data to estimate the current speed of vehicle traffic (or, equivalently, trip travel time). While travel times are useful to individual drivers, the state variable for a large class of traffic models and control algorithms is vehicle density. Our goal is to use probe data to supplement traditional fixed-location loop detector data for density estimation. To this end, we derive a method based on Rao–Blackwellized particle filters, a sequential Monte Carlo scheme. We present a simulation where we obtain a 30% reduction in density mean absolute percentage error from fusing loop and probe data versus using loop data alone. We also present results using real data from a 19-mi freeway section in Los Angeles, CA, where we obtain a 31% reduction. In addition, our method's estimate when using only the real-world probe data and no loop data outperformed the estimate produced when only loop data were used (i.e., an 18% reduction). These results demonstrate that probe data can be used for traffic density estimation.

Connected through hyperedge HE-1 (induced by source paper "a framework for robust assimilation of potentially malign third party data and its statistical meaning"):
  with members:
    - "particle filter enabled real time sensor fault detection without a model of faults"
    - "rao blackwellised particle filtering for dynamic bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "on node models for high dimensional road networks"):
  with members:
    - "expert level control of ramp metering based on multi task deep reinforcement learning"
    - "generic second order macroscopic traffic node model for general multi input multi output road junctions via a dynamic system approach"
    - "a dynamic system characterization of road network node models"

Connected through hyperedge HE-3 (induced by source paper "particle filter enabled real time sensor fault detection without a model of faults"):
  with members:
    - "internet of things iot a vision architectural elements and future directions"

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)