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 "learning co sparse analysis operators with separable structures"):
  Abstract: In the co-sparse analysis model a set of filters is applied to a signal out of the signal class of interest yielding sparse filter responses. As such, it may serve as a prior in inverse problems, or for structural analysis of signals that are known to belong to the signal class. The more the model is adapted to the class, the more reliable it is for these purposes. The task of learning such operators for a given class is therefore a crucial problem. In many applications, it is also required that the filter responses are obtained in a timely manner, which can be achieved by filters with a separable structure. Not only can operators of this sort be efficiently used for computing the filter responses, but they also have the advantage that less training samples are required to obtain a reliable estimate of the operator. The first contribution of this work is to give theoretical evidence for this claim by providing an upper bound for the sample complexity of the learning process. The second is a stochastic gradient descent (SGD) method designed to learn an analysis operator with separable structures, which includes a novel and efficient step size selection rule. Numerical experiments are provided that link the sample complexity to the convergence speed of the SGD algorithm.

Includes members:
  Member "insights into analysis operator learning from patch based sparse models to higher order mrfs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "controlling neural networks via energy dissipation")
  Member "sparse and spurious dictionary learning with noise and outliers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms")
  Member "learning ell_1 based analysis and synthesis sparsity priors using bi level optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "insights into analysis operator learning from patch based sparse models to higher order mrfs")
  Member "separable cosparse analysis operator learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint sensing matrix and sparsifying dictionary optimization for tensor compressive sensing")
  Member "a bimodal co sparse analysis model for image processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "model based learning of local image features for unsupervised texture segmentation")
  Member "separable dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms")
  Member "analysis based blind compressive sensing":
    also connected through other hyperedges:
      (none)
  Member "analysis operator learning and its application to image reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "controlling neural networks via energy dissipation")

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)