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 "on the performance of turbo signal recovery with partial dft sensing matrices"):
  Abstract: This letter is on the performance of the turbo signal recovery (TSR) algorithm for partial discrete Fourier transform (DFT) matrices based compressed sensing. Based on state evolution analysis, we prove that TSR with a partial DFT sensing matrix outperforms the well-known approximate message passing (AMP) algorithm with an independent identically distributed (IID) sensing matrix.

Includes members:
  Member "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ssfn self size estimating feed forward network with low complexity limited need for human intervention and consistent behaviour across trials")
  Member "compressive imaging via approximate message passing with image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "block coordinate regularization by denoising")
  Member "turbo compressed sensing with partial dft sensing matrix":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "macroscopic analysis of vector approximate message passing in a model mismatch setting")
  Member "analysis of compressed sensing with spatially coupled orthogonal matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressed coding amp based decoding and analog spatial coupling")
  Member "s amp approximate message passing for general matrix ensembles":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "near optimal compressed sensing without priors parametric sure approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "amp inspired deep networks for sparse linear inverse problems")
  Member "signal recovery using expectation consistent approximation for linear observations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "macroscopic analysis of vector approximate message passing in a model mismatch setting")
  Member "on sparse vector recovery performance in structurally orthogonal matrices via lasso":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse vector recovery bernoulli gaussian message passing")

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)