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 "near minimax line spectral estimation"):
  Abstract: This paper establishes a nearly optimal algorithm for estimating the frequencies and amplitudes of a mixture of sinusoids from noisy equispaced samples. We derive our algorithm by viewing line spectral estimation as a sparse recovery problem with a continuous, infinite dictionary. We show how to compute the estimator via semidefinite programming and provide guarantees on its mean-square error rate. We derive a complementary minimax lower bound on this estimation rate, demonstrating that our approach nearly achieves the best possible estimation error. Furthermore, we establish bounds on how well our estimator localizes the frequencies in the signal, showing that the localization error tends to zero as the number of samples grows. We verify our theoretical results in an array of numerical experiments, demonstrating that the semidefinite programming approach outperforms two classical spectral estimation techniques.

Includes members:
  Member "forward backward greedy algorithms for atomic norm regularization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "support detection in super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "atomic norm denoising with applications to line spectral estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")
  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 "super resolution from noisy data":
    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 "towards a mathematical theory of super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "coherence pattern guided compressive sensing with unresolved grids":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "conditioning of restricted fourier matrices and super resolution of music")
  Member "how well can we estimate a sparse vector":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an introductory guide to fano s inequality with applications in statistical estimation")

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)