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 iterative message passing decoders with low error floor for ldpc codes"
  Abstract: The error floor phenomenon observed with LDPC codes and their graph-based, iterative, message-passing (MP) decoders is commonly attributed to the existence of error-prone substructures -- variously referred to as near codewords, trapping sets, absorbing sets, or pseudocodewords -- in a Tanner graph representation of the code. Many approaches have been proposed to lower the error floor by designing new LDPC codes with fewer such substructures or by modifying the decoding algorithm. Using a theoretical analysis of iterative MP decoding in an idealized trapping set scenario, we show that a contributor to the error floors observed in the literature may be the imprecise implementation of decoding algorithms and, in particular, the message quantization rules used. We then propose a new quantization method -- (q+1)-bit quasi-uniform quantization -- that efficiently increases the dynamic range of messages, thereby overcoming a limitation of conventional quantization schemes. Finally, we use the quasi-uniform quantizer to decode several LDPC codes that suffer from high error floors with traditional fixed-point decoder implementations. The performance simulation results provide evidence that the proposed quantization scheme can, for a wide variety of codes, significantly lower error floors with minimal increase in decoder complexity.

Connected through hyperedge HE-1 (induced by source paper "deep learning based quantization of l values for gray coded modulation"):
  with members:
    - "deep log likelihood ratio quantization"
    - "deepcode feedback codes via deep learning"
    - "a survey on methods and theories of quantized neural networks"
    - "mimo channel information feedback using deep recurrent network"
    - "an introduction to deep learning for the physical layer"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "neural discrete representation learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "performance bounds and estimates for quantized ldpc decoders"):
  with members:
    - "thresholds of absorbing sets in low density parity check codes"
    - "on characterization of elementary trapping sets of variable regular ldpc codes"
    - "controlling the error floor in ldpc decoding"
    - "error floor approximation for ldpc codes in the awgn channel"
    - "the trapping redundancy of linear block codes"

Connected through hyperedge HE-3 (induced by source paper "study of iterative detection and decoding for large scale mimo systems with 1 bit adcs"):
  with members:
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "a low complexity soft output wmd decoding for uplink mimo systems with one bit adcs"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-4 (induced by source paper "quantized message passing for ldpc codes"):
  with members:
    - "a fully unrolled ldpc decoder based on quantized message passing"
    - "quantization of binary input discrete memoryless channels"

Connected through hyperedge HE-5 (induced by source paper "a fully unrolled ldpc decoder based on quantized message passing"):
  with members:
    - "finite alphabet iterative decoders part ii towards guaranteed error correction of ldpc codes via iterative decoder diversity"
    - "quantization of binary input discrete memoryless channels"

Connected through hyperedge HE-6 (induced by source paper "analysis of saturated belief propagation decoding of low density parity check codes"):
  with members:
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "controlling the error floor in ldpc decoding"
    - "error floor approximation for ldpc codes in the awgn channel"
    - "wave like solutions of general one dimensional spatially coupled systems"
    - "on the capacity of the discrete time channel with uniform output quantization"
    - "on the dynamics of the error floor behavior in regular ldpc codes"
    - "degree optimization and stability condition for the min sum decoder"

Connected through hyperedge HE-7 (induced by source paper "the effect of saturation on belief propagation decoding of ldpc codes"):
  with members:
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "controlling the error floor in ldpc decoding"
    - "error floor approximation for ldpc codes in the awgn channel"
    - "on the capacity of the discrete time channel with uniform output quantization"
    - "on the dynamics of the error floor behavior in regular ldpc codes"
    - "degree optimization and stability condition for the min sum decoder"

Connected through hyperedge HE-8 (induced by source paper "error floor approximation for ldpc codes in the awgn channel"):
  with members:
    - "ldpc code density evolution in the error floor region"
    - "the sum product algorithm for degree 2 check nodes and trapping sets"
    - "numerical issues affecting ldpc error floors"
    - "controlling the error floor in ldpc decoding"
    - "on the dynamics of the error floor behavior in regular ldpc codes"
    - "the trapping redundancy of linear block codes"
    - "informed dynamic scheduling for belief propagation decoding of ldpc codes"
    - "a general method for finding low error rates of ldpc 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.IT(Information Theory)