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: "reduced complexity belief propagation decoders for polar codes"
  Abstract: Polar codes are newly discovered capacity-achieving codes, which have attracted lots of research efforts. Polar codes can be efficiently decoded by the low-complexity successive cancelation (SC) algorithm and the SC list (SCL) decoding algorithm. The belief propagation (BP) decoding algorithm not only is an alternative to the SC and SCL decoders, but also provides soft outputs that are necessary for joint detection and decoding. Both the BP decoder and the soft cancelation (SCAN) decoder were proposed for polar codes to output soft information about the coded bits. In this paper, first a belief propagation decoding algorithm, called reduced complexity soft cancelation (RCSC) decoding algorithm, is proposed. Let N denote the block length. Our RCSC decoding algorithm needs to store only 5N − 3 log-likelihood ratios (LLRs), significantly less than 4N − 2+ N log2N/2 and N(log2 N +1) LLRs needed by the BP and SCAN decoders, respectively, when N ≥ 64. Besides, compared to the SCAN decoding algorithm, our RCSC decoding algorithm eliminates unnecessary additions over the real field. Then the simplified SC (SSC) principle is applied to our RCSC decoding algorithm, and the resulting SSC-aided RCSC (S-RCSC) decoding algorithm further reduces the computational complexity. Finally, based on the S-RCSC decoding algorithm, we propose a corresponding memory efficient decoder architecture, which has better error performance than existing architectures. Besides, our decoder architecture consumes less energy on updating LLRs.

Connected through hyperedge HE-1 (induced by source paper "comparison of polar decoders with existing low density parity check and turbo decoders"):
  with members:
    - "fast low complexity decoders for low rate polar codes"
    - "llr based successive cancellation list decoder for polar codes with multibit decision"
    - "partitioned successive cancellation list decoding of polar codes"
    - "multi mode unrolled architectures for polar decoders"
    - "a high throughput list decoder architecture for polar codes"
    - "hardware decoders for polar codes an overview"
    - "fast list decoders for polar codes"
    - "symbol decision successive cancellation list decoder for polar codes"

Connected through hyperedge HE-2 (induced by source paper "mitigating clipping effects on error floors under belief propagation decoding of polar codes"):
  with members:
    - "comparison of polar decoders with existing low density parity check and turbo decoders"
    - "on deep learning based channel decoding"
    - "improving belief propagation decoding of polar codes using scattered exit charts"
    - "combining belief propagation and successive cancellation list decoding of polar codes on a gpu platform"
    - "flexible length polar codes through graph based augmentation"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "the effect of saturation on belief propagation decoding of ldpc codes"
    - "list decoding of polar codes"

Connected through hyperedge HE-3 (induced by source paper "concatenated ldpc polar codes decoding through belief propagation"):
  with members:
    - "llr based successive cancellation list decoding of polar codes"
    - "xj bp express journey belief propagation decoding for polar codes"
    - "on finite length performance of polar codes stopping sets error floor and concatenated design"
    - "hardware architectures for successive cancellation decoding of polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "polarization for arbitrary discrete 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)