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: "quantization for uplink transmissions in two tier networks with femtocells"
  Abstract: We propose two novel schemes to level up the sum--rate for a two-tier network with femtocell where the backhaul uplink and downlink connecting the Base Stations have limited capacity. The backhaul links are exploited to transport the information in order to improve the decoding of the macrocell and femtocell messages. In the first scheme, Quantize-and-Forward, the Femto Base Station (FBS) quantizes what it receives and forwards it to the Macro Base Station (MBS). Two quantization methods are considered: Elementary Quantization and Wyner-Ziv Quantization. In the second scheme, called Decode-and-Forward with Quantized Side Information (DFQSI) to be distinguished with the considered conventional Decode-and-Forward (DF) scheme. The DFQSI scheme exploits the backhaul downlink to quantize and send the information about the message in the macrocell to the FBS to help it better decode the message, cancel it and decode the message in the femtocell. The results show that there are interesting scenarios in which the proposed techniques offer considerable gains in terms of maximal sum rate and max minimal rate.

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)