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: "theoretical evaluation of offloading through wireless lans"
  Abstract: Offloading of cellular traffic through a wireless local area network (WLAN) is theoretically evaluated. First, empirical data sets of the locations of WLAN internet access points are analyzed and an inhomogeneous Poisson process consisting of high, normal, and low density regions is proposed as a spatial point process model for these configurations. Second, performance metrics, such as mean available bandwidth for a user and the number of vertical handovers, are evaluated for the proposed model through geometric analysis. Explicit formulas are derived for the metrics, although they depend on many parameters such as the number of WLAN access points, the shape of each WLAN coverage region, the location of each WLAN access point, the available bandwidth (bps) of the WLAN, and the shape and available bandwidth (bps) of each subregion identified by the channel quality indicator in a cell of the cellular network. Explicit formulas strongly suggest that the bandwidth a user experiences does not depend on the user mobility. This is because the bandwidth available by a user who does not move and that available by a user who moves are the same or approximately the same as a probabilistic distribution. Numerical examples show that parameters, such as the size of regions where placement of WLAN access points is not allowed and the mean density of WLANs in high density regions, have a large impact on performance metrics. In particular, a homogeneous Poisson process model as the WLAN access point location model largely overestimates the mean available bandwidth for a user and the number of vertical handovers. The overestimated mean available bandwidth is, for example, about 50% in a certain condition.

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.PF(Performance)