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: "convergence of weighted min sum decoding via dynamic programming on trees"
  Abstract: Applying the max-product (and belief-propagation) algorithms to loopy graphs is now quite popular for best assignment problems. This is largely due to their low computational complexity and impressive performance in practice. Still, there is no general understanding of the conditions required for convergence and/or the optimality of converged solutions. This paper presents an analysis of both attenuated max-product (AMP) decoding and weighted min-sum (WMS) decoding for LDPC codes which guarantees convergence to a fixed point when a weight parameter, {\beta}, is sufficiently small. It also shows that, if the fixed point satisfies some consistency conditions, then it must be both the linear-programming (LP) and maximum-likelihood (ML) solution. #R##N#For (dv,dc)-regular LDPC codes, the weight must satisfy {\beta}(dv-1) \leq 1 whereas the results proposed by Frey and Koetter require instead that {\beta}(dv-1)(dc-1)   1 is also given. Finally, connections are explored with recent work by Arora et al. on the threshold of LP decoding.

Connected through hyperedge HE-1 (induced by source paper "hierarchies of local optimality characterizations in decoding of tanner codes"):
  with members:
    - "on decoding irregular tanner codes with local optimality guarantees"
    - "exchange of limits why iterative decoding works"

Connected through hyperedge HE-2 (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"
    - "correcting a fraction of errors in nonbinary expander codes with linear programming"
    - "lp decoding of regular ldpc codes in memoryless channels"
    - "error bounds for repeat accumulate codes decoded via linear programming"
    - "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 "local optimality certificates for lp decoding of tanner codes"):
  with members:
    - "lp decoding of regular ldpc codes in memoryless channels"
    - "error bounds for repeat accumulate codes decoded via linear programming"
    - "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)