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 "joint sparse channel estimation and decoding continuous and discrete domain sparsity"):
  Abstract: Recent progress in wireless receiver design has been towards iterative processing, where channel estimation and decoding is considered a joint optimization problem. Sparse channel estimation is another recent advancement, which exploits the inherent structure of those wireless channels that are composed of a small number of multipath components. In this work we design iterative receivers which incorporate sparse channel estimation. State-of-the-art sparse channel estimators simplify the estimation problem to be a finite basis selection problem by restricting the multipath delays to the discrete domain (i.e. to a grid). Our main contribution is a receiver without such a restriction; the delays are estimated directly as continuous values. As a result, our receiver does not suffer from the leakage effect which destroys sparsity when the delays are restricted to the discrete domain. We discuss certain connections between continuous and discrete domain sparse estimation methods. Our receivers outperform state-of-the-art sparse channel estimation iterative receivers in terms of bit error rate.

Includes members:
  Member "variational bayesian inference of line spectra":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "expectation propagation line spectral estimation")
  Member "atomic norm denoising with applications to line spectral estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")
  Member "fast and robust parametric estimation of jointly sparse channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "super resolution phase retrieval for sparse signals")
  Member "message passing algorithms for channel estimation and decoding using approximate inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unifying message passing algorithms under the framework of constrained bethe free energy minimization")
  Member "towards a mathematical theory of super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "a message passing receiver for bicm ofdm over unknown clustered sparse channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity optimality of amp in coded systems")
  Member "merging belief propagation and the mean field approximation a free energy approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dnn aided block sparse bayesian learning for user activity detection and channel estimation in grant free non orthogonal random access")
  Member "compressive estimation of doubly selective channels in multicarrier systems leakage effects and sparsity enhancing processing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "eliminating impulsive noise in pilot aided ofdm channels via dual of penalized atomic norm")

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)