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: "auctioning based coordinated tv white space spectrum sharing for home networks"
  Abstract: The idea of having the geolocation database monitor the secondary use of TV white space (TVWS) spectrum and assist in coordinating the secondary usage is gaining ground. Considering the home networking use case, we leverage the geolocation database for interference-aware coordinated TVWS sharing among secondary users (home networks) using {\em short-term auctions}, thereby realize a dynamic secondary market. To enable this auctioning based coordinated TVWS sharing framework, we propose an enhanced {\em market-driven TVWS spectrum access model}. For the short-term auctions, we propose an online multi-unit, iterative truthful mechanism called VERUM that takes into consideration spatially heterogeneous spectrum availability, an inherent characteristic in the TVWS context. We prove that VERUM is truthful (i.e., the best strategy for every bidder is to bid based on its true valuation) and is also efficient in that it allocates spectrum to users who value it the most. Evaluation results from scenarios with real home distributions in urban and dense-urban environments and using realistic TVWS spectrum availability maps show that VERUM performs close to optimal allocation in terms of revenue for the coordinating spectrum manager. Comparison with two existing efficient and truthful multi-unit spectrum auction schemes, VERITAS and SATYA, shows that VERUM fares better in terms of revenue, spectrum utilisation and percentage of winning bidders in diverse conditions. Taking all of the above together, VERUM can be seen to offer incentives to subscribed users encouraging them to use TVWS spectrum through greater spectrum availability (as measured by percentage of winning bidders) as well as to the coordinating spectrum manager through revenue generation.

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)