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: "simultaneous codeword optimization simco for dictionary update and learning"
  Abstract: We consider the data-driven dictionary learning problem. The goal is to seek an over-complete dictionary from which every training signal can be best approximated by a linear combination of only a few codewords. This task is often achieved by iteratively executing two operations: sparse coding and dictionary update. The focus of this paper is on the dictionary update step, where the dictionary is optimized with a given sparsity pattern. We propose a novel framework where an arbitrary set of codewords and the corresponding sparse coefficients are simultaneously updated, hence the term simultaneous codeword optimization (SimCO). The SimCO formulation not only generalizes benchmark mechanisms MOD and K-SVD, but also allows the discovery that singular points, rather than local minima, are the major bottleneck of dictionary update. To mitigate the problem caused by the singular points, regularized SimCO is proposed. First and second order optimization procedures are designed to solve regularized SimCO. Simulations show that regularization substantially improves the performance of dictionary learning.

Connected through hyperedge HE-1 (induced by source paper "online convex matrix factorization with representative regions"):
  with members:
    - "a ranking model motivated by nonnegative matrix factorization with applications to tennis tournaments"
    - "symmetry saddle points and global geometry of nonconvex matrix factorization"
    - "sparse approximation via generating point sets"
    - "convex sparse matrix factorizations"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-2 (induced by source paper "joint sensing matrix and sparsifying dictionary optimization for tensor compressive sensing"):
  with members:
    - "compressive sensing of sparse tensors"
    - "separable cosparse analysis operator learning"
    - "optimized projections for compressed sensing via rank constrained nearest correlation matrix"
    - "projection design for statistical compressive sensing a tight frame based approach"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "compressed sensing of simultaneous low rank and joint sparse matrices"
    - "iterative hard thresholding for compressed sensing"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "adaptive dictionary based approach for background noise and speaker classification and subsequent source separation"):
  with members:
    - "a dictionary learning and source recovery based approach to classify diverse audio sources"
    - "supervised and unsupervised speech enhancement using nonnegative matrix factorization"

Connected through hyperedge HE-4 (induced by source paper "sparse bayesian dictionary learning with a gaussian hierarchical model"):
  with members:
    - "cloud k svd a collaborative dictionary learning algorithm for big distributed data"
    - "pattern coupled sparse bayesian learning for recovery of block sparse signals"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-5 (induced by source paper "ell_1 k svd a robust dictionary learning algorithm with simultaneous update"):
  with members:
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "manopt a matlab toolbox for optimization on manifolds"

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)