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: "multi cell device to device communications a spectrum sharing and densification study"
  Abstract: One of the most significant 5G technology enablers will be Device-to-Device (D2D) communications. D2D communications constitute a promising way to improve spectral, energy and latency performance, exploiting the physical proximity of communicating devices and increasing resource utilization. Furthermore, network infrastructure densification has been considered as one of the most substantial methods to increase system performance, taking advantage of base station proximity and spatial reuse of system resources. However, could we improve system performance by leveraging both of these two 5G enabling technologies together in a multi-cell environment? How does spectrum sharing affect performance enhancement? This article investigates the implications of interference, densification and spectrum sharing in D2D performance gain. The in-band D2D approach, where legacy users coexist with potential D2D pairs, is considered in a multi-cell system. Overlay and underlay spectrum sharing approaches are employed in order for the potential D2D pairs to access the spectrum. Given that two of the most critical problems in the D2D concept are mode selection and user scheduling, we jointly address them, aiming at maximizing the total system uplink throughput. Thus, we present a radio resource management mechanism for intra-cell and cross-cell overlay/underlay D2D communications enabled in a multi-cell system. System-level simulations are executed to evaluate the system performance and examine the trends of D2D communication gain for the different spectrum sharing approaches and various densification scenarios. Finally, realworld SDR-based experiments are performed to test and assess D2D communications for overlay and underlay spectrum sharing.

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.IT(Information Theory)