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 "functional nonlinear sparse models"):
  Abstract: Signal processing is rich in inherently continuous and often nonlinear applications, such as spectral estimation, optical imaging, and super-resolution microscopy, in which sparsity plays a key role in obtaining state-of-the-art results. Coping with the infinite dimensionality and non-convexity of these estimation problems typically involves discretization and convex relaxations, e.g., using atomic norms. Nevertheless, grid mismatch and other coherence issues imply that discretized sparse signals are often no longer sparse. Even if they are, recovering sparse solutions using convex relaxations requires assumptions that may be hard to meet in practice. Finally, problems involving nonlinear measurements remain non-convex even after relaxing the sparsity objective. We address these issues by directly tackling the continuous, nonlinear problem cast as a sparse functional optimization program. We prove that when these problems are non-atomic, they have no duality gap and can therefore be solved efficiently using duality and~(stochastic) convex optimization methods. We illustrate the wide range of applications of this approach by formulating and solving problems from nonlinear spectral estimation and robust classification.

Includes members:
  Member "learning power spectrum maps from quantized power measurements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "iterative channel estimation for discrete denoising under channel uncertainty")
  Member "recipes for stable linear embeddings from hilbert spaces to ℝ m":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimality of 1 norm regularization among weighted 1 norms for sparse recovery a case study on how to find optimal regularizations")
  Member "block iterative reweighted algorithms for super resolution of spectrally sparse signals":
    also connected through other hyperedges:
      (none)
  Member "forward backward greedy algorithms for atomic norm regularization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "computational complexity of certifying restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximately certifying the restricted isometry property is hard")
  Member "on gridless sparse methods for line spectral estimation from complete and incomplete data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "expectation propagation line spectral estimation")
  Member "breaking the coherence barrier a new theory for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable")
  Member "atomic norm denoising with applications to line spectral estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")

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.LG(Machine Learning)