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: "to relay or not to relay in cognitive radio sensor networks"
  Abstract: Recent works proposed the relaying at the MAC layer in cognitive radio networks whereby the primary packets are forwarded by the secondary node maintaining an extra queue devoted to the relaying function. However, relaying of primary packets may introduce delays on the secondary packets (called secondary delay) and require additional power budget in order to forward the primary packets that is especially crucial when the network is deployed using sensors with limited power resources. To this end, an admission control can be employed in order to manage efficiently the relaying in cognitive radio sensor networks. In this paper, we first analyse and formulate the secondary delay and the required power budget of the secondary sensor node in relation with the acceptance factor that indicates whether the primary packets are allowed to be forwarded or not. Having defined the above, we present the tradeoff between the secondary delay and the required power budget when the acceptance factor is adapted. In the sequel, we formulate an optimization problem to minimize the secondary delay over the admission control parameter subject to a limit on the required power budget plus the constraints related to the stabilities of the individual queues due to their interdependencies observed by the analysis. The solution of this problem is provided using iterative decomposition methods i.e. dual and primal decompositions using Lagrange multipliers that simplifies the original complicated problem resulting in a final equivalent dual problem that includes the initial Karush Kuhn Tucker conditions. Using the derived equivalent dual problem, we obtain the optimal acceptance factor while in addition we highlight the possibilities for extra delay minimization that is provided by relaxing the initial constraints through changing the values of the Lagrange multipliers.

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)