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: "improving convergence of belief propagation decoding"
  Abstract: The decoding of Low-Density Parity-Check codes by the Belief Propagation (BP) algorithm is revisited. We check the iterative algorithm for its convergence to a codeword (termination), we run Monte Carlo simulations to find the probability distribution function of the termination time, n_it. Tested on an example [155, 64, 20] code, this termination curve shows a maximum and an extended algebraic tail at the highest values of n_it. Aiming to reduce the tail of the termination curve we consider a family of iterative algorithms modifying the standard BP by means of a simple relaxation. The relaxation parameter controls the convergence of the modified BP algorithm to a minimum of the Bethe free energy. The improvement is experimentally demonstrated for Additive-White-Gaussian-Noise channel in some range of the signal-to-noise ratios. We also discuss the trade-off between the relaxation parameter of the improved iterative scheme and the number of iterations.

Connected through hyperedge HE-1 (induced by source paper "a two stage selective averaging ldpc decoding"):
  with members:
    - "the trapping redundancy of linear block codes"
    - "informed dynamic scheduling for belief propagation decoding of ldpc codes"

Connected through hyperedge HE-2 (induced by source paper "the trapping redundancy of linear block codes"):
  with members:
    - "permutation decoding and the stopping redundancy hierarchy of cyclic and extended cyclic codes"
    - "on parity check collections for iterative erasure decoding that correct all correctable erasure patterns of a given size"
    - "on the hardness of approximating stopping and trapping sets in ldpc codes"
    - "shortened array codes of large girth"
    - "a general computation rule for lossy summaries messages with examples from equalization"
    - "characterizations of pseudo codewords of ldpc codes"
    - "on the stopping distance and the stopping redundancy of codes"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes"

Connected through hyperedge HE-3 (induced by source paper "searching for low weight pseudo codewords"):
  with members:
    - "pseudo codeword landscape"
    - "guessing facets polytope structure and improved lp decoder"
    - "loop calculus helps to improve belief propagation and linear programming decodings of low density parity check codes"
    - "towards low complexity linear programming decoding"
    - "an efficient pseudo codeword search algorithm for linear programming decoding of ldpc codes"
    - "instanton analysis of low density parity check codes in the error floor regime"
    - "how to find good finite length codes from art towards science"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes"

Connected through hyperedge HE-4 (induced by source paper "reducing the error floor"):
  with members:
    - "pseudo codeword landscape"
    - "guessing facets polytope structure and improved lp decoder"
    - "loop calculus helps to improve belief propagation and linear programming decodings of low density parity check codes"
    - "an efficient pseudo codeword search algorithm for linear programming decoding of ldpc codes"
    - "how to find good finite length codes from art towards science"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes"
    - "the error floor of ldpc codes in the laplacian channel"

Connected through hyperedge HE-5 (induced by source paper "loop calculus helps to improve belief propagation and linear programming decodings of low density parity check codes"):
  with members:
    - "guessing facets polytope structure and improved lp decoder"
    - "towards low complexity linear programming decoding"
    - "instanton analysis of low density parity check codes in the error floor regime"
    - "characterizations of pseudo codewords of ldpc codes"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes"
    - "the benefit of thresholding in lp decoding 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)