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: "polar coded non orthogonal multiple access"
  Abstract: Non-orthogonal multiple access (NOMA) is one of the key techniques to address the high spectral efficiency and massive connectivity requirements for the fifth generation (5G) wireless networks. To efficiently realize NOMA, we propose a framework combining the binary polar coding and the NOMA transmission, which adequately utilizes the reliability distinctions among users. In this polar-coded NOMA (PC-NOMA) system, the NOMA transmission is recognized from the novel perspective of channel polarization. Combing the binary polar coding and signal modulation, the original NOMA channel is decomposed into multiple bit polarized channels by using a three-stage channel transform, that is, user  $\rightarrow$  signal   $\rightarrow$  bit partitions. Specifically, for the first stage channel transform, we design two schemes, namely the sequential user partition and the parallel user partition (PUP). For the SUP based PC-NOMA system, a “worst-goes-first” strategy is proposed to determine the user partition order which improves the system performance by the enhanced polarization effect among the user synthesized channels. Also, its performance is analyzed by using the channel polarization principle. In the receiver, a joint successive cancellation detection and decoding scheme is developed. For the PUP based PC-NOMA system, a parallel detection scheme is exploited to reduce the processing latency. Furthermore, a round-robin user scheduling is given for the above two PC-NOMA systems, which ensures the user fairness over multiple data blocks. The block error ratio and throughput performances indicate that the proposed PC-NOMA systems obviously outperform the state-of-the-art turbo-coded NOMA systems due to the advantages of joint design between the polar coding and the NOMA transmission.

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)