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 "support localization and the fisher metric for off the grid sparse regularization"):
  Abstract: Sparse regularization is a central technique for both machine learning (to achieve supervised features selection or unsupervised mixture learning) and imaging sciences (to achieve super-resolution). Existing performance guaranties assume a separation of the spikes based on an ad-hoc (usually Euclidean) minimum distance condition, which ignores the geometry of the problem. In this article, we study the BLASSO (i.e. the off-the-grid version of $\ell^1$ LASSO regularization) and show that the Fisher-Rao distance is the natural way to ensure and quantify support recovery, since it preserves the invariance of the problem under reparameterization. We prove that under mild regularity and curvature conditions, stable support identification is achieved even in the presence of randomized sub-sampled observations (which is the case in compressed sensing or learning scenario). On deconvolution problems, which are translation invariant, this generalizes to the multi-dimensional setting existing results of the literature. For more complex translation-varying problems, such as Laplace transform inversion, this gives the first geometry-aware guarantees for sparse recovery.

Includes members:
  Member "stable separation and super resolution of mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi kernel unmixing and super resolution using the modified matrix pencil method")
  Member "robust recovery of stream of pulses using convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the stable resolution limit of total variation regularization for spike deconvolution")
  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 "breaking the curse of dimensionality with convex neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "good subnetworks provably exist pruning via greedy forward selection")
  Member "a two round variant of em for gaussian mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "super resolution from noisy data":
    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 "towards a mathematical theory of super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "a probabilistic and ripless theory of compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")

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)