human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on the sample complexity of predictive sparse coding"
  Abstract: The goal of predictive sparse coding is to learn a representation of examples as sparse linear combinations of elements from a dictionary, such that a learned hypothesis linear in the new representation performs well on a predictive task. Predictive sparse coding algorithms recently have demonstrated impressive performance on a variety of supervised tasks, but their generalization properties have not been studied. We establish the first generalization error bounds for predictive sparse coding, covering two settings: 1) the overcomplete setting, where the number of features k exceeds the original dimensionality d; and 2) the high or infinite-dimensional setting, where only dimension-free bounds are useful. Both learning bounds intimately depend on stability properties of the learned sparse encoder, as measured on the training sample. Consequently, we first present a fundamental stability result for the LASSO, a result characterizing the stability of the sparse codes with respect to perturbations to the dictionary. In the overcomplete setting, we present an estimation error bound that decays as \tilde{O}(sqrt(d k/m)) with respect to d and k. In the high or infinite-dimensional setting, we show a dimension-free bound that is \tilde{O}(sqrt(k^2 s / m)) with respect to k and s, where s is an upper bound on the number of non-zeros in the sparse code for any training data point.

Connected through hyperedge HE-1 (induced by source paper "convergence radius and sample complexity of itkm algorithms for dictionary learning"):
  with members:
    - "simple efficient and neural algorithms for sparse coding"
    - "complete dictionary recovery over the sphere"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "more algorithms for provable dictionary learning"
    - "local identification of overcomplete dictionaries"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "on iterative hard thresholding methods for high dimensional m estimation"

Connected through hyperedge HE-2 (induced by source paper "sparse and spurious dictionary learning with noise and outliers"):
  with members:
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "local identification of overcomplete dictionaries"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "sparse topical coding"
    - "exact recovery of sparsely used dictionaries"
    - "optimization with sparsity inducing penalties"
    - "on the local correctness of l 1 minimization for dictionary learning"

Connected through hyperedge HE-3 (induced by source paper "local identification of overcomplete dictionaries"):
  with members:
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "exact recovery of sparsely used dictionaries"
    - "on the local correctness of l 1 minimization for dictionary learning"

Connected through hyperedge HE-4 (induced by source paper "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"):
  with members:
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "exact recovery of sparsely used dictionaries"
    - "on the local correctness of l 1 minimization for dictionary learning"

Question: Please tell me which class the center node 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)