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 "minimax lower bounds for kronecker structured dictionary learning"):
  Abstract: Dictionary learning is the problem of estimating the collection of atomic elements that provide a sparse representation of measured/collected signals or data. This paper finds fundamental limits on the sample complexity of estimating dictionaries for tensor data by proving a lower bound on the minimax risk. This lower bound depends on the dimensions of the tensor and parameters of the generative model. The focus of this paper is on second-order tensor data, with the underlying dictionaries constructed by taking the Kronecker product of two smaller dictionaries and the observed data generated by sparse linear combinations of dictionary atoms observed through white Gaussian noise. In this regard, the paper provides a general lower bound on the minimax risk and also adapts the proof techniques for equivalent results using sparse and Gaussian coefficient models. The reported results suggest that the sample complexity of dictionary learning for tensor data can be significantly lower than that for unstructured data.

Includes members:
  Member "minimax lower bounds on dictionary learning for tensor data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms")
  Member "a tensor based dictionary learning approach to tomographic image reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non negative tensor patch dictionary approaches for image compression and deblurring applications")
  Member "denoising and completion of 3d data via multidimensional dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms")
  Member "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms")
  Member "local identification of overcomplete dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "minimax lower bounds on dictionary learning for tensor data")
  Member "sparse and spurious dictionary learning with noise and outliers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms")
  Member "new algorithms for learning incoherent and overcomplete dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when can wasserstein gans minimize wasserstein distance")
  Member "separable dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms")

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)