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: "precoded gfdm system to combat inter carrier interference performance analysis"
  Abstract: The expected operating scenarios of 5G pose a great challenge to orthogonal frequency division multiplexing (OFDM) which has poor out of band (OoB) spectral properties, stringent synchronization requirements, and large symbol duration. Generalized frequency division multiplexing (GFDM) which is the focus of this work, has been suggested in the literature as one of the possible solutions to meet 5G requirements. In this work, the analytical performance evaluation of MMSE receiver for GFDM is presented. We also proposed precoding techniques to enhance the performance of GFDM. A simplified expression of SINR for MMSE receiver of GFDM is derived using special properties related to the modulation matrix of GFDM, which are described in this work. This SINR is used to evaluate the BER performance. Precoding schemes are proposed to reduce complexity of GFDM-MMSE receiver without compromising on the performance. Block Inverse Discrete Fourier Transform (BIDFT) and Discrete Fourier Transform (DFT) based precoding schemes are found to outperform GFDM-MMSE receiver due to frequency diversity gain while having complexity similar to zero-forcing receiver of GFDM. It is shown that both BIDFT and DFT-based precoding schemes reduce peak to average power ratio (PAPR) significantly. Computational complexity of different transmitters and receivers of precoded and uncoded GFDM is also presented.

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)