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: "on the capacity of the discrete time channel with uniform output quantization"
  Abstract: This paper provides new insight into the classical problem of determining both the capacity of the discrete-time channel with uniform output quantization and the capacity achieving input distribution. It builds on earlier work by Gallager and Witsenhausen to provide a detailed analysis of two particular quantization schemes. The first is saturation quantization where overflows are mapped to the nearest quantization bin, and the second is wrapping quantization where overflows are mapped to the nearest quantization bin after reduction by some modulus. Both the capacity of wrapping quantization and the capacity achieving input distribution are determined. When the additive noise is gaussian and relatively small, the capacity of saturation quantization is shown to be bounded below by that of wrapping quantization. In the limit of arbitrarily many uniform quantization levels, it is shown that the difference between the upper and lower bounds on capacity given by Ihara is only 0.26 bits.

Connected through hyperedge HE-1 (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"
    - "quantized iterative message passing decoders with low error floor for ldpc codes"
    - "wave like solutions of general one dimensional spatially coupled systems"
    - "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-2 (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"
    - "quantized iterative message passing decoders with low error floor for ldpc codes"
    - "on the dynamics of the error floor behavior in regular ldpc codes"
    - "degree optimization and stability condition for the min sum decoder"

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)