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: "degree optimization and stability condition for the min sum decoder"
  Abstract: The min-sum (MS) algorithm is arguably the second most fundamental algorithm in the realm of message passing due to its optimality (for a tree code) with respect to the {\em block error} probability \cite{Wiberg}. There also seems to be a fundamental relationship of MS decoding with the linear programming decoder \cite{Koetter}. Despite its importance, its fundamental properties have not nearly been studied as well as those of the sum-product (also known as BP) algorithm. #R##N#We address two questions related to the MS rule. First, we characterize the stability condition under MS decoding. It turns out to be essentially the same condition as under BP decoding. Second, we perform a degree distribution optimization. Contrary to the case of BP decoding, under MS decoding the thresholds of the best degree distributions for standard irregular LDPC ensembles are significantly bounded away from the Shannon threshold. More precisely, on the AWGN channel, for the best codes that we find, the gap to capacity is 1dB for a rate 0.3 code and it is 0.4dB when the rate is 0.9 (the gap decreases monotonically as we increase the rate). #R##N#We also used the optimization procedure to design codes for modified MS algorithm where the output of the check node is scaled by a constant $1/\alpha$. For $\alpha = 1.25$, we observed that the gap to capacity was lesser for the modified MS algorithm when compared with the MS algorithm. However, it was still quite large, varying from 0.75 dB to 0.2 dB for rates between 0.3 and 0.9. #R##N#We conclude by posing what we consider to be the most important open questions related to the MS algorithm.

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 capacity of the discrete time channel with uniform output quantization"
    - "on the dynamics of the error floor behavior in regular ldpc codes"

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 capacity of the discrete time channel with uniform output quantization"
    - "on the dynamics of the error floor behavior in regular 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)