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: "cooperative spectrum sensing scheduling in multi channel cognitive radio networks a broad perspective"
  Abstract: Cooperative spectrum sensing has been proven to improve sensing performance of cognitive users in presence of spectral diversity. For multi-channel CRN (MC-CRN), designing a cooperative spectrum sensing scheme becomes quite challenging as it needs an optimal sensing scheduling scheme, which schedules cognitive users to different channels such that a good balance between detection performance and the discovery of spectrum holes is achieved. The main issue associated with cooperative spectrum sensing scheduling (CSSS) scheme is the design of an optimal schedule that could specify which SUs should be assigned to which channels at what time to achieve maximal network throughput with minimal amount of energy and satisfying the desirable sensing accuracy. In this regard, designing an efficient CSSS scheme for MC-CRN is of utmost importance for practical implementation of CRN. In this article, we explore CSSS problem from a broad perspective and present the different objectives and the inherent issues and challenges arise in designing the CSSS scheme. We also discuss the different methods used for modeling of the CSSS scheme for MC-CRN. Further, a few future research directions are listed which need investigation while developing the solution for CSSS problem.

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)