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: "distance properties of short ldpc codes and their impact on the bp ml and near ml decoding performance"
  Abstract: Parameters of LDPC codes, such as minimum distance, stopping distance, stopping redundancy, girth of the Tanner graph, and their influence on the frame error rate performance of the BP, ML and near-ML decoding over a BEC and an AWGN channel are studied. Both random and structured LDPC codes are considered. In particular, the BP decoding is applied to the code parity-check matrices with an increasing number of redundant rows, and the convergence of the performance to that of the ML decoding is analyzed. A comparison of the simulated BP, ML, and near-ML performance with the improved theoretical bounds on the error probability based on the exact weight spectrum coefficients and the exact stopping size spectrum coefficients is presented. It is observed that decoding performance very close to the ML decoding performance can be achieved with a relatively small number of redundant rows for some codes, for both the BEC and the AWGN channels.

Connected through hyperedge HE-1 (induced by source paper "stopping redundancy hierarchy beyond the minimum distance"):
  with members:
    - "on the pseudocodeword redundancy of binary linear codes"
    - "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"
    - "generating parity check equations for bounded distance iterative erasure decoding"
    - "on the stopping distance and the stopping redundancy of codes"
    - "improved upper bounds on stopping redundancy"

Connected through hyperedge HE-2 (induced by source paper "ml and near ml decoding of ldpc codes over the bec bounds and decoding algorithms"):
  with members:
    - "searching for voltage graph based ldpc tailbiting codes with large girth"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "decoding of convolutional codes over the erasure channel"
    - "analysis of quasi cyclic ldpc codes under ml decoding over the erasure channel"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"

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)