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: "queuing theory guided intelligent traffic scheduling through video analysis using dirichlet process mixture model"
  Abstract: Abstract   Intelligent traffic signaling is an important part of city road traffic management systems. In many countries, it is done through supervised/semi-supervised ways. With the advances in computer vision and machine learning, it is now possible to develop expert systems guided intelligent traffic signaling systems that are unsupervised in nature. In order to schedule traffic signals, it is essential to learn the traffic characterization parameters such as the number of vehicles, their arrival and departure rates, etc. In this work, we use unsupervised machine learning with the help of a modified Dirichlet Process Mixture Model (DPMM) to measure the aforementioned traffic parameters. This has been done using a new feature, named temporal clusters or tracklets extracted using DPMM. Detailed analysis on tracklet behavior during signal on/off period has been carried out to derive a queuing theory-based method for signal duration prediction. The queuing behavior at a junction is analyzed using tracklets for understanding their applicability. Queue clearance time at the junction has been used for predicting the signal duration with the help of Gaussian regression of historical data.  Two publicly available video datasets, namely QMUL and MIT have been used for verification of the hypothesis. The mean absolute error (MAE) of the proposed method using tracklets has been reduced by a factor of 2.4 and 6.3 when compared with the tracks generated using Kernel Correlation Filters (KCF) and Kanade–Lucas–Tomasi (KLT), respectively. Through experiments, we are also able to establish that KCF and KLT tracks do not consider spatial occupancy of the vehicles on roads, leading to error in the estimation. The results reveal that the proposed queuing theory-based approach predicts the signal duration for the next cycle more accurately as compared to the ground truths. The method can be used for building intelligent traffic control systems for roadway junctions in cities and highways.

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.CV(Computer Vision and Pattern Recognition)