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 spatially coupled sparse superposition codes with amp decoding"):
  Abstract: Sparse superposition codes (SPARCs) are a class of codes for efficient communication over the AWGN channel at rates approaching the channel capacity. In a standard SPARC, codewords are sparse linear combinations of columns of an i.i.d. Gaussian design matrix, while in a spatially coupled SPARC, the design matrix has a block-wise structure where the variance of the Gaussian entries can be varied across blocks. A well-designed spatial coupling structure can enhance the error performance of iterative decoding algorithms such as Approximate Message Passing (AMP). In this paper, we obtain a non-asymptotic bound on the probability of error of spatially coupled SPARCs with AMP decoding. Applying this bound to a simple band-diagonal design matrix, we prove that spatially coupled SPARCs with AMP decoding achieve the capacity of the AWGN channel. The bound also highlights how the decay of error probability depends on each design parameter of the spatially coupled SPARC. #R##N#The asymptotic mean squared error (MSE) of the AMP decoder can be predicted via a deterministic recursion called state evolution. Our result provides the first proof that the MSE concentrates on the state evolution prediction for spatially coupled SPARCs. Combined with the state evolution prediction, the result implies that spatially coupled SPARCs are capacity-achieving. The proof technique used to establish the main result is also used to obtain a concentration inequality for the MSE of AMP applied to compressed sensing with spatially coupled design matrices. Finally, we provide numerical simulation results that demonstrate the finite length error performance of spatially coupled SPARCs. The performance is compared with coded modulation schemes that use LDPC codes from the DVB-S2 standard.

Includes members:
  Member "sparse regression codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "the error probability of sparse superposition codes with approximate message passing decoding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse regression codes")
  Member "spatially coupled sparse regression codes design and state evolution analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse regression codes")
  Member "finite sample analysis of approximate message passing algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inference with deep generative priors in high dimensions")
  Member "techniques for improving the finite length performance of sparse superposition codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on approximate message passing for unsourced access with coded compressed sensing")
  Member "approximate message passing decoder and capacity achieving sparse superposition codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compressed coding amp based decoding and analog spatial coupling")
  Member "capacity achieving sparse superposition codes via approximate message passing decoding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressed coding amp based decoding and analog spatial coupling")
  Member "universal sparse superposition codes with spatial coupling and gamp decoding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compressed coding amp based decoding and analog spatial coupling")

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)