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 "capacity achieving sparse superposition codes via approximate message passing decoding"):
  Abstract: Sparse superposition codes were recently introduced by Barron and Joseph for reliable communication over the additive white Gaussian noise (AWGN) channel at rates approaching the channel capacity. The codebook is defined in terms of a Gaussian design matrix, and codewords are sparse linear combinations of columns of the matrix. In this paper, we propose an approximate message passing decoder for sparse superposition codes, whose decoding complexity scales linearly with the size of the design matrix. The performance of the decoder is rigorously analyzed and it is shown to asymptotically achieve the AWGN capacity with an appropriate power allocation. Simulation results are provided to demonstrate the performance of the decoder at finite blocklengths. We introduce a power allocation scheme to improve the empirical performance, and demonstrate how the decoding complexity can be significantly reduced by using Hadamard design matrices.

Includes members:
  Member "approximate message passing decoder and capacity achieving sparse superposition codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "construction of capacity achieving lattice codes polar lattices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse regression codes")
  Member "replica analysis and approximate message passing decoder for superposition codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse regression codes")
  Member "approximate message passing with consistent parameter estimation and applications to sparse learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "information theoretically optimal compressed sensing via spatial coupling and approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "a message passing receiver for bicm ofdm over unknown clustered sparse channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity optimality of amp in coded systems")
  Member "the dynamics of message passing on dense graphs with applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")

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)