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: "time domain n continuous gfdm"
  Abstract: Generalized frequency division multiplexing (GFDM) has been a candidate multicarrier scheme in the 5th generation cellular networks for its flexibility of transmitter filter in time and frequency. However, for the circularly shaped transmitter filter, GFDM provides limited performance gain of sidelobe suppression. In this paper, we propose a scheme, called time-domain N-continuous GFDM (TD-NC-GFDM), to reduce the discontinuities caused by the GFDM transmitter filter and achieve promising sidelobe suppression gain. Based on time-domain N-continuous orthogonal frequency devision multiplexing (TD-NC-OFDM), TD-NC-GFDM signal can be obtained by superposing a smooth signal in the time domain. The smooth signal is linearly combined by basis signals in a new basis set related to GFDM transmitter waveform. To eliminate the interference caused by the smooth signal, two solutions are proposed. Firstly, a signal recovery algorithm for reception is adopted at the cost of high complexity. Thus, secondly, to simplify the TD-NC-GFDM receiver, a low-interference TD-NC-GFDM is proposed by redesigning the basis signals. A soft truncation of the basis signals in TD-NC-GFDM is given to design the basis signals in the low-interference TD-NC-GFDM. Then, the smooth signal is aligned with the beginning of the GFDM symbol and is added in the front part of the GFDM symbol. Moreover, for a big number of GFDM subsymbols, theoretical analysis proves that the signal-to-interference ratio (SIR) in TD-NC-GFDM is much higher than that in TD-NC-OFDM. Simulation results shows that TD-NC-GFDM can obtain significant sidelobe suppression performance as well as the low-interference TD-NC-GFDM, which can achieve the same BER performance as the original GFDM.

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)