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 over the bec"):
  Abstract: We present the tree-structure expectation propagation (Tree-EP) algorithm to decode low-density parity-check (LDPC) codes over discrete memoryless channels (DMCs). Expectation propagation generalizes 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 pairwise marginal constraints over pairs of variables connected to a check node of the LDPC code's Tanner graph. Thanks to these additional constraints, the Tree-EP marginal estimates for each variable in the graph are more accurate than those provided by BP. We also reformulate the Tree-EP algorithm for the binary erasure channel (BEC) as a peeling-type algorithm (TEP) and we show that the algorithm has the same computational complexity as BP and it decodes a higher fraction of errors. We describe the TEP decoding process by a set of differential equations that represents the expected residual graph evolution as a function of the code parameters. The solution of these equations is used to predict the TEP decoder performance in both the asymptotic regime and the finite-length regimes over the BEC. While the asymptotic threshold of the TEP decoder is the same as the BP decoder for regular and optimized codes, we propose a scaling law for finite-length LDPC codes, which accurately approximates the TEP improved performance and facilitates its optimization.

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 "analytical solution of covariance evolution for irregular ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a scaling law to predict the finite length performance of spatially coupled ldpc codes")
  Member "structured region graphs morphing ep into gbp":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "region based approximation for high dimensional bayesian network models")
  Member "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "tree structure expectation propagation for decoding ldpc codes over binary erasure channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distance properties of short ldpc codes and their impact on the bp ml and near ml decoding performance")
  Member "accumulate repeat accumulate codes capacity achieving ensembles of systematic codes for the erasure channel with bounded complexity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "log logarithmic time pruned polar coding")
  Member "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reed muller codes theory and algorithms")
  Member "on the stopping distance and the stopping redundancy of codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "decoder tailored polar code design using the genetic algorithm")

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)