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 "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"):
  Abstract: Abstract   This article gives theoretical insights into the performance of K-SVD, a dictionary learning algorithm that has gained significant popularity in practical applications. The particular question studied here is when a dictionary    Φ  ∈    R    d  ×  K      can be recovered as local minimum of the minimisation criterion underlying K-SVD from a set of N training signals      y    n    =  Φ    x    n     . A theoretical analysis of the problem leads to two types of identifiability results assuming the training signals are generated from a tight frame with coefficients drawn from a random symmetric distribution. First, asymptotic results showing that in expectation the generating dictionary can be recovered exactly as a local minimum of the K-SVD criterion if the coefficient distribution exhibits sufficient decay. Second, based on the asymptotic results it is demonstrated that given a finite number of training samples N, such that    N  /  log  ⁡  N  =  O  (    K    3    d  )   , except with probability    O  (    N    −  K  d    )    there is a local minimum of the K-SVD criterion within distance    O  (  K    N    −  1  /  4    )    to the generating dictionary.

Includes members:
  Member "new algorithms for learning incoherent and overcomplete dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when can wasserstein gans minimize wasserstein distance")
  Member "learning sparsely used overcomplete dictionaries via alternating minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "on the sample complexity of predictive sparse coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convergence radius and sample complexity of itkm algorithms for dictionary learning")
  Member "exact recovery of sparsely used dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "on the local correctness of l 1 minimization for dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convergence radius and sample complexity of itkm algorithms for dictionary learning")

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)