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: "information centric offloading in cellular networks with coordinated device to device communication"
  Abstract: In this paper, we develop a comprehensive analytical framework for cache enabled cellular networks overlaid with coordinated device-to-device (D2D) communication. We follow an approach similar to LTE Direct, where the base station (BS) is responsible for establishing D2D links. We consider that an arbitrary requesting user is offloaded to D2D mode to communicate with one of its 'k' closest D2D helpers within the macrocell subject to content availability and helper selection scheme. We consider two different D2D helper selection schemes: 1) uniform selection (US), where the D2D helper is selected uniformly and 2) nearest selection (NS), where the nearest helper possessing the content is selected. Employing tools from stochastic geometry, we model the locations of BSs and D2D helpers using independent homogeneous Poisson point processes (HPPPs). We characterize the D2D mode probability of an arbitrary user for both the NS and US schemes. The distribution of the distance between an arbitrary user and its ith neighboring D2D helper within the macrocell is derived using disk approximation for the Voronoi cell, which is shown to be reasonably accurate. We fully characterize the overall coverage probability and the average ergodic rate of an arbitrary user requesting a particular content. We show that significant performance gains can be achieved compared to conventional cellular communication under both the NS and US schemes when popular contents are requested and NS scheme always outperforms the US scheme. Our analysis reveals an interesting trade off between the performance metrics and the number of candidate D2D helpers 'k'. We conclude that enhancing D2D opportunities for the users does not always result in better performance and the network parameters have to be carefully tuned to harness maximum gains.

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)