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 "low rank tensor recovery via iterative hard thresholding"):
  Abstract: We study extensions of compressive sensing and low rank matrix recovery (matrix completion) to the recovery of low rank tensors of higher order from a small number of linear measurements. While the theoretical understanding of low rank matrix recovery is already well-developed, only few contributions on the low rank tensor recovery problem are available so far. In this paper, we introduce versions of the iterative hard thresholding algorithm for several tensor decompositions, namely the higher order singular value decomposition (HOSVD), the tensor train format (TT), and the general hierarchical Tucker decomposition (HT). We provide a partial convergence result for these algorithms which is based on a variant of the restricted isometry property of the measurement operator adapted to the tensor decomposition at hand that induces a corresponding notion of tensor rank. We show that subgaussian measurement ensembles satisfy the tensor restricted isometry property with high probability under a certain almost optimal bound on the number of measurements which depends on the corresponding tensor format. These bounds are extended to partial Fourier maps combined with random sign flips of the tensor entries. Finally, we illustrate the performance of iterative hard thresholding methods for tensor recovery via numerical experiments where we consider recovery from Gaussian random measurements, tensor completion (recovery of missing entries), and Fourier measurements for third order tensors.

Includes members:
  Member "tensor theta norms and low rank recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "simultaneous structures in convex signal recovery revisiting the convex combination of norms")
  Member "the restricted isometry property of subsampled fourier matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "one shot neural architecture search via compressive sensing")
  Member "stable low rank matrix recovery via null space properties":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an analysis of noise folding for low rank matrix recovery")
  Member "tensor prediction rademacher complexity and random 3 xor":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "extended formulation lower bounds for refuting random csps")
  Member "approximation algorithms for model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stochastic iterative hard thresholding for graph structured sparsity optimization")
  Member "dimensionality reduction with subgaussian matrices a unified theory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications")
  Member "structured random measurements in signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications")
  Member "low rank matrix recovery from rank one measurements":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")

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)