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: "analysis framework for opportunistic spectrum ofdma and its application to the ieee 802 22 standard"
  Abstract: We present an analytical model that enables the evaluation of opportunistic spectrum orthogonal frequency division multiple-access (OS-OFDMA) networks using metrics such as blocking probability or, most importantly, throughput. The core feature of the model, based on a discrete-time Markov chain, is the consideration of different channel and subchannel allocation strategies under different primary and secondary user types, traffic, and priority levels. The analytical model also assesses the impact of different spectrum sensing strategies on the throughput of OS-OFDMA network. In addition, we consider studies of cochannel interference. The analysis is applied to the IEEE 802.22 standard to evaluate the impact of the two-stage spectrum sensing strategy and the varying temporal activity of wireless microphones on the system throughput. In addition to the analytical model, we present a set of comprehensive simulation results using NS-2 related to the delay performance of the OS-OFDMA system considered. Our study suggests that OS-OFDMA with subchannel notching and channel bonding could provide almost ten times higher throughput compared with a design without these options when the activity and density of wireless microphones are very high. Furthermore, we confirm that OS-OFDMA implementation without subchannel notching, which is used in the IEEE 802.22, can support the real-time and non-real-time quality of service classes, provided that the temporal activity of wireless microphones is moderate (with sparse wireless microphone distribution, with light urban population density and short duty cycles). Finally, the two-stage spectrum sensing option improves the OS-OFDMA throughput, provided that the length of spectrum sensing at every stage is optimized using our model.

Connected through hyperedge HE-1 (induced by source paper "when channel bonding is beneficial for opportunistic spectrum access networks"):
  with members:
    - "primary user traffic estimation for dynamic spectrum access"
    - "performance of joint spectrum sensing and mac algorithms for multichannel opportunistic spectrum access ad hoc networks"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

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)