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: "error bounds for repeat accumulate codes decoded via linear programming"
  Abstract: We examine regular and irregular repeat-accumulate (RA) codes with repetition degrees which are all even. For these codes and with a particular choice of an interleaver, we give an upper bound on the decoding error probability of a linear-programming based decoder which is an inverse polynomial in the block length. Our bound is valid for any memoryless, binary-input, output-symmetric (MBIOS) channel. This result generalizes the bound derived by Feldman et al., which was for regular RA(2) codes.

Connected through hyperedge HE-1 (induced by source paper "mathematical programming decoding of binary linear codes theory and algorithms"):
  with members:
    - "polytope of correct linear programming decoding and low weight pseudo codewords"
    - "efficient linear programming decoding of hdpc codes"
    - "efficient implementation of linear programming decoding"
    - "a decomposition theory for binary linear codes"
    - "max product for maximum weight matching convergence correctness and lp duality"
    - "interior point algorithms for linear programming decoding"
    - "a cutting plane method based on redundant rows for improving fractional distance"
    - "linear programming decoding of nonbinary linear codes"

Connected through hyperedge HE-2 (induced by source paper "local optimality guarantees for optimal decoding based on paths"):
  with members:
    - "correcting a fraction of errors in nonbinary expander codes with linear programming"
    - "on decoding irregular tanner codes with local optimality guarantees"
    - "lp decoding of regular ldpc codes in memoryless channels"
    - "probabilistic analysis of linear programming decoding"
    - "on the block error probability of lp decoding of ldpc 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 "on decoding irregular tanner codes with local optimality guarantees"):
  with members:
    - "counting in graph covers a combinatorial characterization of the bethe entropy function"
    - "convergence of weighted min sum decoding via dynamic programming on trees"
    - "correcting a fraction of errors in nonbinary expander codes with linear programming"
    - "lp decoding of regular ldpc codes in memoryless channels"
    - "probabilistic analysis of linear programming decoding"
    - "on the block error probability of lp decoding of ldpc codes"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes"

Connected through hyperedge HE-4 (induced by source paper "local optimality certificates for lp decoding of tanner codes"):
  with members:
    - "convergence of weighted min sum decoding via dynamic programming on trees"
    - "lp decoding of regular ldpc codes in memoryless channels"
    - "on the block error probability of lp decoding of ldpc codes"
    - "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)