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: "is non neutrality profitable for the stakeholders of the internet market"
  Abstract: In this part of the paper, we obtain analytical results for the case that transport costs are not small (complement of Part I), and combine them with the results in Part I of the paper to provide general results for all values of transport costs. We show that, in general, if an SPNE exists, it would be one of the five possible strategies each of which we explicitly characterize. We also prove that when EUs have sufficiently high inertia for at least one of the ISPs, there exists a unique SPNE with a non-neutral outcome in which both of the ISPs are active, and the CP offers her content with free quality on the neutral ISP and with premium quality on the non-neutral ISP. Moreover, we show that an SPNE does not always exist. We also analyze a benchmark case in which both ISPs are neutral, and prove that there exists a unique SPNE in which the CP offers her content with free quality on both ISPs, and both ISPs are active. We also provide extensive numerical results and discussions for all ranges of transport costs. Simulation results suggest that if the SPNE exists, it would be unique. In addition, results reveal that the neutral ISP receives a lower payoff and the non-neutral ISP receives a higher payoff (most of the time) in a non-neutral scenario. However, we also identify scenarios in which the non-neutral ISP loses payoff by adopting non-neutrality. In addition, we show that a non-neutral regime yields a higher welfare for EUs than a neutral one if the market power of the non-neutral ISP is small, the sensitivity of EUs (respectively, the CP) to the quality is low (respectively, high), or a combinations of these factors.

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.GT(Computer Science and Game Theory)