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: "sample complexity of bayesian optimal dictionary learning"
  Abstract: We consider a learning problem of identifying a dictionary matrix D (M times N dimension) from a sample set of M dimensional vectors Y = N^{-1/2} DX, where X is a sparse matrix (N times P dimension) in which the density of non-zero entries is 0 rho is satisfied in the limit of N to infinity. Our analysis also implies that the posterior distribution given Y is condensed only at the correct dictionary D when the compression rate alpha is greater than a certain critical value alpha_M(rho). This suggests that belief propagation may allow us to learn D with a low computational complexity using O(N) samples.

Connected through hyperedge HE-1 (induced by source paper "phase transitions and sample complexity in bayes optimal matrix factorization"):
  with members:
    - "an overview of low rank matrix recovery from incomplete observations"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "information theoretically optimal sparse pca"
    - "bilinear generalized approximate message passing part i derivation"
    - "variational free energies for compressed sensing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "phase diagram and approximate message passing for blind calibration and dictionary learning"

Connected through hyperedge HE-2 (induced by source paper "phase diagram and approximate message passing for blind calibration and dictionary learning"):
  with members:
    - "compressed sensing under matrix uncertainty optimum thresholds and robust approximate message passing"
    - "optimal phase transitions in compressed sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "on the local correctness of l 1 minimization for dictionary learning"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "iterative reconstruction of rank one matrices in noise"):
  with members:
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "information theoretically optimal sparse pca"
    - "bilinear generalized approximate message passing part i derivation"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"

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)