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: "an efficient pseudo codeword search algorithm for linear programming decoding of ldpc codes"
  Abstract: In Linear Programming (LP) decoding of a Low-Density-Parity-Check (LDPC) code one minimizes a linear functional, with coefficients related to log-likelihood ratios, over a relaxation of the polytope spanned by the codewords \cite{03FWK}. In order to quantify LP decoding, and thus to describe performance of the error-correction scheme at moderate and large Signal-to-Noise-Ratios (SNR), it is important to study the relaxed polytope to understand better its vertexes, so-called pseudo-codewords, especially those which are neighbors of the zero codeword. In this manuscript we propose a technique to heuristically create a list of these neighbors and their distances. Our pseudo-codeword-search algorithm starts by randomly choosing the initial configuration of the noise. The configuration is modified through a discrete number of steps. Each step consists of two sub-steps. Firstly, one applies an LP decoder to the noise-configuration deriving a pseudo-codeword. Secondly, one finds configuration of the noise equidistant from the pseudo codeword and the zero codeword. The resulting noise configuration is used as an entry for the next step. The iterations converge rapidly to a pseudo-codeword neighboring the zero codeword. Repeated many times, this procedure is characterized by the distribution function (frequency spectrum) of the pseudo-codeword effective distance. The effective distance of the coding scheme is approximated by the shortest distance pseudo-codeword in the spectrum. The efficiency of the procedure is demonstrated on examples of the Tanner $[155,64,20]$ code and Margulis $p=7$ and $p=11$ codes (672 and 2640 bits long respectively) operating over an Additive-White-Gaussian-Noise (AWGN) channel.

Connected through hyperedge HE-1 (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"
    - "instanton analysis of low density parity check codes in the error floor regime"
    - "improving convergence of belief propagation decoding"
    - "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-2 (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"
    - "improving convergence of belief propagation decoding"
    - "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-3 (induced by source paper "pseudo codeword landscape"):
  with members:
    - "loop calculus helps to improve belief propagation and linear programming decodings of low density parity check codes"
    - "towards low complexity linear programming decoding"
    - "instanton analysis of low density parity check codes in the error floor regime"
    - "graph cover decoding and finite length analysis of message passing iterative 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)