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 "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion"):
  Abstract: A spectrally sparse signal of order $r$ is a mixture of $r$ damped or undamped complex sinusoids. This paper investigates the problem of reconstructing spectrally sparse signals from a random subset of $n$ regular time domain samples, which can be reformulated as a low rank Hankel matrix completion problem. We introduce an iterative hard thresholding (IHT) algorithm and a fast iterative hard thresholding (FIHT) algorithm for efficient reconstruction of spectrally sparse signals via low rank Hankel matrix completion. Theoretical recovery guarantees have been established for FIHT, showing that $O(r^2\log^2(n))$ number of samples are sufficient for exact recovery with high probability. Empirical performance comparisons establish significant computational advantages for IHT and FIHT. In particular, numerical simulations on $3$D arrays demonstrate the capability of FIHT on handling large and high-dimensional real data.

Includes members:
  Member "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "atomic norm denoising for complex exponentials with unknown waveform modulations")
  Member "robust spectral compressed sensing via structured matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recovery analysis of damped spectrally sparse signals and its relation to music")
  Member "music for single snapshot spectral estimation stability and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the stable resolution limit of total variation regularization for spike deconvolution")
  Member "compressed sensing off the grid":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "beyond nyquist efficient sampling of sparse bandlimited signals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices")
  Member "guaranteed rank minimization via singular value projection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "a simpler approach to matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "federated over the air subspace learning from incomplete data")
  Member "iterative hard thresholding for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")

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)