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: "local loss optimization in operator models a new insight into spectral learning"
  Abstract: This paper re-visits the spectral method for learning latent variable models defined in terms of observable operators. We give a new perspective on the method, showing that operators can be recovered by minimizing a loss defined on a finite subset of the domain. A non-convex optimization similar to the spectral method is derived. We also propose a regularized convex relaxation of this optimization. We show that in practice the availabilty of a continuous regularization parameter (in contrast with the discrete number of states in the original method) allows a better trade-off between accuracy and model complexity. We also prove that in general, a randomized strategy for choosing the local loss will succeed with high probability.

Connected through hyperedge HE-1 (induced by source paper "a maximum matching algorithm for basis selection in spectral learning"):
  with members:
    - "visualizing and understanding recurrent networks"
    - "dimension free concentration bounds on hankel matrices for spectral learning"
    - "a method of moments for mixture models and hidden markov models"
    - "reduced rank hidden markov models"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-2 (induced by source paper "efficient learning and planning with compressed predictive states"):
  with members:
    - "hilbert space embeddings of predictive state representations"
    - "pegasus a policy search method for large mdps and pomdps"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "a spectral learning approach to range only slam"
    - "a method of moments for mixture models and hidden markov models"
    - "bellman error based feature generation using random projections on sparse spaces"
    - "predictive state temporal difference learning"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-3 (induced by source paper "on learning parametric output hmms"):
  with members:
    - "a method of moments for mixture models and hidden markov models"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"
    - "reduced rank hidden markov models"
    - "a spectral algorithm for learning hidden markov models"
    - "learning nonsingular phylogenies and hidden markov models"

Connected through hyperedge HE-4 (induced by source paper "dimension free concentration bounds on hankel matrices for spectral learning"):
  with members:
    - "a method of moments for mixture models and hidden markov models"
    - "a spectral algorithm for latent dirichlet allocation"
    - "reduced rank hidden markov models"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-5 (induced by source paper "tensor decompositions for learning latent variable models"):
  with members:
    - "smoothed analysis of tensor decompositions"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "a method of moments for mixture models and hidden markov models"
    - "a spectral algorithm for latent dirichlet allocation"
    - "learning topic models going beyond svd"
    - "provable ica with unknown gaussian noise and implications for gaussian mixtures and autoencoders"
    - "settling the polynomial learnability of mixtures of gaussians"

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)