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: "tree structure expectation propagation for decoding ldpc codes over binary erasure channels"
  Abstract: Expectation Propagation is a generalization to Belief Propagation (BP) in two ways. First, it can be used with any exponential family distribution over the cliques in the graph. Second, it can impose additional constraints on the marginal distributions. We use this second property to impose pair-wise marginal distribution constraints in some check nodes of the LDPC Tanner graph. These additional constraints allow decoding the received codeword when the BP decoder gets stuck. In this paper, we first present the new decoding algorithm, whose complexity is identical to the BP decoder, and we then prove that it is able to decode codewords with a larger fraction of erasures, as the block size tends to infinity. The proposed algorithm can be also understood as a simplification of the Maxwell decoder, but without its computational complexity. We also illustrate that the new algorithm outperforms the BP decoder for finite block-size codes.

Connected through hyperedge HE-1 (induced by source paper "distance properties of short ldpc codes and their impact on the bp ml and near ml decoding performance"):
  with members:
    - "ml and near ml decoding of ldpc codes over the bec bounds and decoding algorithms"
    - "refined upper bounds on stopping redundancy of binary linear codes"
    - "on the pseudocodeword redundancy of binary linear codes"
    - "permutation decoding and the stopping redundancy hierarchy of cyclic and extended cyclic 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-2 (induced by source paper "bp led decoding algorithm for ldpc codes over awgn channels"):
  with members:
    - "new techniques for upper bounding the ml decoding performance of binary linear codes"
    - "weight distributions of regular low density parity check codes over finite fields"
    - "lowering the error floor of ldpc codes using cyclic liftings"
    - "tightened upper bounds on the ml decoding error probability of binary linear block codes"

Connected through hyperedge HE-3 (induced by source paper "tree structure expectation propagation for ldpc decoding over the bec"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "analytical solution of covariance evolution for irregular ldpc codes"
    - "structured region graphs morphing ep into gbp"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "accumulate repeat accumulate codes capacity achieving ensembles of systematic codes for the erasure channel with bounded complexity"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "on the stopping distance and the stopping redundancy of codes"
    - "finite length scaling for iteratively decoded ldpc ensembles"

Connected through hyperedge HE-4 (induced by source paper "tree structure expectation propagation for ldpc decoding in erasure channels"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "finite length scaling for iteratively decoded ldpc ensembles"
    - "randomized algorithms for the loop cutset problem"

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)