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 "low complexity iterative algorithms for discrete compressed sensing"):
  Abstract: We consider iterative (`turbo') algorithms for compressed sensing. First, a unified exposition of the different approaches available in the literature is given, thereby enlightening the general principles and main differences. In particular we discuss i) the estimation step (matched filter vs. optimum MMSE estimator), ii) the unbiasing operation (implicitly or explicitly done and equivalent to the calculation of extrinsic information), and iii) thresholding vs. the calculation of soft values. Based on these insights we propose a low-complexity but well-performing variant utilizing a Krylov space approximation of the optimum linear MMSE estimator. The derivations are valid for any probability density of the signal vector. However, numerical results are shown for the discrete case. The novel algorithms shows very good performance and even slightly faster convergence compared to approximative message passing.

Includes members:
  Member "algorithms for the iterative estimation of discrete valued sparse vectors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unveiling bias compensation in turbo based algorithms for discrete compressed sensing")
  Member "turbo compressed sensing with partial dft sensing matrix":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "macroscopic analysis of vector approximate message passing in a model mismatch setting")
  Member "on the performance of turbo signal recovery with partial dft sensing matrices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structured turbo compressed sensing for downlink massive mimo ofdm channel estimation")
  Member "the dynamics of message passing on dense graphs with applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressed sensing with invertible generative models and dependent noise")
  Member "message passing algorithms for compressed sensing i motivation and construction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "random access for massive machine type communications")
  Member "on the role of mmse estimation in approaching the information theoretic limits of linear gaussian channels shannon meets wiener":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lattice coding for downlink multiuser transmission")

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)