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: "load aware performance analysis of cell center edge users in random hetnets"
  Abstract: For real-time traffic, the link quality and call blocking probability (both derived from coverage probability) are realized to be poor for cell edge users (CEUs) compared to cell center users (CCUs) as the signal reception in the cell center region is better compared to the cell edge region. In heterogeneous networks (HetNets), the uncoordinated channel access by different types of base stations determine the interference statistics that further arbitrates the coverage probability. Thus, the spectrum allocation techniques have major impact on the performance of CCU and CEU. In this paper, the performance of CCUs and CEUs in a random two-tier network is studied for two spectrum allocation techniques namely: 1) co-channel (CSA), and 2) shared (SSA). For performance analysis, the widely accepted conception of modeling the tiers of HetNet using independent homogeneous Poisson point process (PPP) is considered to accommodate the spatial randomness in location of BSs. To incorporate the spatial randomness in the arrival of service and to aid the load-aware analysis, the cellular traffic is modeled using spatio-temporal PPP. Under this scenario, we have developed an analytical framework to evaluate the load-aware performance, including coverage and blocking probabilities, of CCUs and CEUs under both spectrum allocation techniques. Further, we provide insight into achievable area energy efficiency for SSA and CSA. The developed analytical framework is validated through extensive simulations. Next, we demonstrate the impact of traffic load and femto access points density on the performance of CCUs/CEUs under CSA and SSA.

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)