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: "selective data collection in vehicular networks for traffic control applications"
  Abstract: Abstract   Vehicular sensor network (VSN) is an emerging technology, which combines wireless communication offered by vehicular ad hoc networks (VANETs) with sensing devices installed in vehicles. VSN creates a huge opportunity to extend the road-side sensor infrastructure of existing traffic control systems. The efficient use of the wireless communication medium is one of the basic issues in VSN applications development. This paper introduces a method of selective data collection for traffic control applications, which provides a significant reduction in data amounts transmitted through VSN. The underlying idea is to detect the necessity of data transfers on the basis of uncertainty determination of the traffic control decisions. According to the proposed approach, sensor data are transmitted from vehicles to the control node only at selected time moments. Data collected in VSN are processed using on-line traffic simulation technique, which enables traffic flow prediction, performance evaluation of control actions and uncertainty estimation. If precision of the resulting information is insufficient, the optimal control action cannot be derived without ambiguity. As a result the control decision becomes uncertain and it is a signal informing that new traffic data from VSN are necessary to provide more precise prediction and to reduce the uncertainty of decision. The proposed method can be applied in traffic control systems of different types e.g. traffic signals, variable speed limits, and dynamic route guidance. The effectiveness of this method is illustrated in an experimental study on traffic control at signalised intersection.

Connected through hyperedge HE-1 (induced by source paper "data suppression algorithms for surveillance applications of wireless sensor and actor networks"):
  with members:
    - "communication aware algorithms for target tracking in wireless sensor networks"
    - "optimizing data collection for object tracking in wireless sensor networks"
    - "uncertainty dependent data collection in vehicular sensor networks"

Connected through hyperedge HE-2 (induced by source paper "uncertainty based information extraction in wireless sensor networks for control applications"):
  with members:
    - "uncertainty dependent data collection in vehicular sensor networks"
    - "wireless sensor actuator network design for mobile control applications"

Connected through hyperedge HE-3 (induced by source paper "a self organizing system for urban traffic control based on predictive interval microscopic model"):
  with members:
    - "a traffic model based on fuzzy cellular automata"

Connected through hyperedge HE-4 (induced by source paper "a traffic model based on fuzzy cellular automata"):
  with members:
    - "performance evaluation of road traffic control using a fuzzy cellular model"
    - "a real time vehicle detection algorithm for vision based sensors"
    - "fuzzy cellular model for on line traffic simulation"

Connected through hyperedge HE-5 (induced by source paper "uncertainty dependent data collection in vehicular sensor networks"):
  with members:
    - "fuzzy cellular model of signal controlled traffic stream"
    - "performance evaluation of road traffic control using a fuzzy cellular model"
    - "fuzzy cellular model for on line traffic simulation"

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)