human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "tree structure expectation propagation for ldpc decoding in erasure channels"):
  Abstract: In this paper we present a new algorithm, denoted as TEP, to decode low-density parity-check (LDPC) codes over the Binary Erasure Channel (BEC). The TEP decoder is derived applying the expectation propagation (EP) algorithm with a tree- structured approximation. Expectation Propagation (EP) 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 constraints in some check nodes of the LDPC code's Tanner graph. The algorithm has the same computational complexity than BP, but it can decode a higher fraction of errors when applied over the BEC. In this paper, we focus on the asymptotic performance of the TEP decoder, as the block size tends to infinity. We describe the TEP decoder by a set of differential equations that represents the residual graph evolution during the decoding process. The solution of these equations yields the capacity of this decoder for a given LDPC ensemble over the BEC. We show that the achieved capacity with the TEP is higher than the BP capacity, at the same computational complexity.

Includes members:
  Member "expectation propagation for approximate bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization")
  Member "tree structure expectation propagation for decoding ldpc codes over binary erasure channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distance properties of short ldpc codes and their impact on the bp ml and near ml decoding performance")
  Member "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reed muller codes theory and algorithms")
  Member "finite length scaling for iteratively decoded ldpc ensembles":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finite length scaling of spatially coupled ldpc codes under window decoding over the bec")
  Member "randomized algorithms for the loop cutset problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hitting topological minors is fpt")

Question: Please tell me which class the source paper 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)