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: "quantized polar code decoders analysis and design"
  Abstract: Applications of massive machine-type communications, such as sensor networks, smart metering, 'internet-of-things', or process and factory automation, are forecast to have great economic impact in the next five to ten years. Low-complexity energy- and cost-efficient communication schemes are essential to enable large-scale deployments. To target these requirements, we study decoding of polar codes with coarse quantization in the short block length regime. In particular, we devise schemes to mitigate the impact of coarse quantization, which has not been adequately explored in prior works. We introduce the 3-level quantized successive cancellation (SC) and SC list (SCL) decoder. Coarse quantization of log-likelihood ratios (LLRs) leads to quantization of path metrics (PMs). Quantized PMs severely impact the list management of SCL decoders, and hence cause performance degradation. Two mitigation strategies are presented: 1.) Selecting the winning codeword from the decoder's list based on maximum-likelihood (ML) rather than PM. 2.) Utilizing statistical knowledge about the reliability of bit estimates in each decoding step to improve list management. We demonstrate the effectiveness of our techniques in simulations. In particular, our enhancements prove useful in the low code rate regime, where theory available in the literature predicts pronounced losses caused by quantization. Furthermore, we put our work into perspective by comparing it to finer quantization and partially unquantized schemes. This yields insights and recommendations as to which quantization schemes offer the best cost-benefit ratio for practical implementation.

Connected through hyperedge HE-1 (induced by source paper "finite level quantization procedures for construction and decoding of polar codes"):
  with members:
    - "efficient polar code construction for higher order modulation"
    - "llr based successive cancellation list decoding of polar codes"
    - "on the construction of polar codes"
    - "polar codes robustness of the successive cancellation decoder with respect to quantization"
    - "how to construct polar codes"
    - "hardware architectures for successive cancellation decoding of polar codes"
    - "performance and construction of polar codes on symmetric binary input memoryless channels"
    - "on the rate of channel polarization"

Connected through hyperedge HE-2 (induced by source paper "ternary quantized polar code decoders analysis and design"):
  with members:
    - "polarbear a 28 nm fd soi asic for decoding of polar codes"
    - "hardware decoders for polar codes an overview"
    - "llr based successive cancellation list decoding of polar codes"
    - "list decoding of polar codes"
    - "polar codes robustness of the successive cancellation decoder with respect to quantization"
    - "hardware architectures for successive cancellation decoding of polar codes"
    - "performance and construction of polar codes on symmetric binary input memoryless channels"

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)