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: "medium access control for dynamic spectrum sharing in cognitive radio networks"
  Abstract: The proliferation of wireless services and applications over the past decade has#R##N#led to the rapidly increasing demand in wireless spectrum. Hence, we have been#R##N#facing a critical spectrum shortage problem even though several measurements#R##N#have indicated that most licensed radio spectrum is very underutilized. These#R##N#facts have motivated the development of dynamic spectrum access (DSA) and#R##N#cognitive radio techniques to enhance the efficiency and flexibility of spectrum#R##N#utilization.#R##N#In this dissertation, we investigate design, analysis, and optimization issues for#R##N#joint spectrum sensing and cognitive medium access control (CMAC) protocol#R##N#engineering for cognitive radio networks (CRNs). The joint spectrum sensing#R##N#and CMAC design is considered under the interweave spectrum sharing paradigm#R##N#and different communications settings. Our research has resulted in four major#R##N#research contributions, which are presented in four corresponding main chapters#R##N#of this dissertation.#R##N#First, we consider the CMAC protocol design with parallel spectrum sensing for#R##N#both single-channel and multi-channel scenarios, which is presented in Chapter#R##N#5. The considered setting captures the case where each secondary user (SU) is#R##N#equipped with multiple transceivers to perform sensing and access of spectrum#R##N#holes on several channels simultaneously.#R##N#Second, we study the single-transceiver-based CMAC protocol engineering for#R##N#hardware-constrained CRNs, which is covered in Chapter 6. In this setting,#R##N#each SU performs sequential sensing over the assigned channels and access one#R##N#available channel for communication by using random access. We also investigate#R##N#the channel assignment problem for SUs to maximize the network throughput.#R##N#Third, we design a distributed framework integrating our developed CMAC protocol#R##N#and cooperative sensing for multi-channel and heterogeneous CRNs, which#R##N#is presented in details in Chapter 7. The MAC protocol is based on the p-persistent#R##N#carrier sense multiple access (CSMA) mechanism and a general cooperative#R##N#sensing adopting the a-out-of-b aggregation rule is employed. Moreover,#R##N#impacts of reporting errors in the considered cooperative sensing scheme are also#R##N#investigated.#R##N#Finally, we propose an asynchronous Full–Duplex cognitive MAC (FDC-MAC)#R##N#exploiting the full-duplex (FD) capability of SUs’ radios for simultaneous spectrum#R##N#sensing and access. The research outcomes of this research are presented in#R##N#Chapter 8. Our design enables to timely detect the PUs’ activity during transmission#R##N#and adaptive reconfigure the sensing time and SUs’ transmit powers to#R##N#achieve the best performance. Therefore, the proposed FDC–MAC protocol is#R##N#more general and flexible compared with existing FD CMAC protocols proposed#R##N#in the literature.#R##N#We develop various analytical models for throughput performance analysis of our#R##N#proposed CMAC protocol designs. Based on these analytical models, we develop#R##N#different efficient algorithms to configure the CMAC protocol including channel#R##N#allocation, sensing time, transmit power, contention window to maximize the#R##N#total throughput of the secondary network. Furthermore, extensive numerical#R##N#results are presented to gain further insights and to evaluate the performance of#R##N#our CMAC protocol designs. Both the numerical and simulation results confirm#R##N#that our proposed CMAC protocols can achieve efficient spectrum utilization and#R##N#significant performance gains compared to existing and unoptimized designs.

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)