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 "exit chart analysis of turbo compressed sensing using message passing dequantization"):
  Abstract: We propose a joint compressed sensing-based encoding and an iterative-decoding method, which we call turbo compressed sensing (turbo-CS), for the robust to noise transmission of sparse signals over an additive white Gaussian noise (AWGN) channel. The turbo-CS encoder applies 1-bit compressed sensing as a source encoder concatenated serially with a convolutional channel encoder. At the turbo-CS decoder, an iterative joint source-channel decoding method is proposed for signal reconstruction. We analyze, for the first time, the convergence of the turbo-CS decoder by determining an extrinsic information transfer chart of the constituent decoders. We modify the soft-outputs of the constituent source decoder to improve the signal reconstruction performance of the turbo-CS decoder. Our results show that for a fixed received signal to noise ratio (RSNR) of   $ \mathbf {10}$    dB more than   $ \mathbf {5}$    dB of improvement in the channel SNR is achieved after six iterations of the turbo-CS decoder. Alternatively, for a fixed SNR of   $ \mathbf {-1}$    dB,   $ \mathbf {10}$   dB improvement in RSNR is achieved. Moreover, it is shown that the turbo-CS decoder outperforms the state-of-the-art algorithms for 1-bit compressed sensing reconstruction in the presence of AWGN channel in terms of signal reconstruction performance and complexity.

Includes members:
  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-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "iterative detection for compressive sensing turbo cs":
    also connected through other hyperedges:
      (none)
  Member "joint source channel vector quantization for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "power constrained sparse gaussian linear dimensionality reduction over noisy channels")
  Member "message passing de quantization with applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "expectation propagation line spectral estimation")
  Member "regime change bit depth versus measurement rate in compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quantized compressed sensing by rectified linear units")
  Member "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "serial quantization for sparse time sequences")
  Member "generalized approximate message passing for estimation with random linear mixing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "estimation with random linear mixing belief propagation and compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems")

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)