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 robust spectrally sparse signal recovery a provable non convex approach via robust low rank hankel matrix reconstruction"):
  Abstract: Consider a spectrally sparse signal $\boldsymbol{x}$ that consists of $r$ complex sinusoids with or without damping. We study the robust recovery problem for the spectrally sparse signal under the fully observed setting, which is about recovering $\boldsymbol{x}$ and a sparse corruption vector $\boldsymbol{s}$ from their sum $\boldsymbol{z}=\boldsymbol{x}+\boldsymbol{s}$. In this paper, we exploit the low-rank property of the Hankel matrix constructed from $\boldsymbol{x}$, and develop an efficient non-convex algorithm, coined Accelerated Alternating Projections for Robust Low-Rank Hankel Matrix Reconstruction (AAP-Hankel). The high computational efficiency and low space complexity of AAP-Hankel are achieved by fast computations involving structured matrices, and a subspace projection method for accelerated low-rank approximation. Theoretical recovery guarantee with a linear convergence rate has been established for AAP-Hankel. Empirical performance comparisons on synthetic and real-world datasets demonstrate the computational advantages of AAP-Hankel, in both efficiency and robustness aspects.

Includes members:
  Member "analog to digital cognitive radio sampling detection and hardware":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the capacity of memoryless channels with sampled cyclostationary gaussian noise")
  Member "accelerated alternating projections for robust principal component analysis":
    also connected through other hyperedges:
      (none)
  Member "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exact matrix completion based on low rank hankel structure in the fourier domain")
  Member "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")
  Member "robust spectral compressed sensing via structured matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "on the stable resolution limit of total variation regularization for spike deconvolution")
  Member "fixed rank matrix factorizations and riemannian low rank optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning multilingual word embeddings in latent metric space a geometric approach")
  Member "compressed sensing and matrix completion with constant proportion of corruptions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks")

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)