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: "a 9 52 db ncg fec scheme and 162 b cycle low complexity product decoder architecture"
  Abstract: Powerful forward error correction (FEC) schemes are used in optical communications to achieve bit-error rates (BERs) below    $10^{-15}$   . These FECs follow one of two approaches: the concatenation of simpler hard-decision codes or the usage of inherently powerful soft-decision codes. The first approach yields lower net coding gains (NCGs), but can usually work at higher code rates and have lower complexity decoders. In this paper, we propose a novel FEC scheme based on a product code and a post-processing technique. It can achieve an NCG of 9.52 dB at a BER of    $10^{-15}$    and 9.96 dB at a BER of    $10^{-18}$   , an error-correction performance that sits between that of current hard-decision and soft-decision FECs. A decoder architecture is designed, tested on field programmable gate array and synthesized in 65-nm CMOS technology: its 162 b/cycle worst-case information throughput can reach 100 Gb/s at the achieved frequency of 616 MHz. Its complexity is shown to be lower than that of hard-decision decoders in literature, and an order of magnitude lower than the estimated complexity of soft-decision decoders.

Connected through hyperedge HE-1 (induced by source paper "approaching miscorrection free performance of product and generalized product codes"):
  with members:
    - "miscorrection free decoding of staircase codes"
    - "stall pattern avoidance in polynomial product codes"
    - "irregular product codes"
    - "staircase codes fec for 100 gb s otn"
    - "approaching capacity at high rates with iterative hard decision decoding"
    - "on the asymptotic performance of iterative decoders for product codes"

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.AR(Hardware Architecture)