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: "reduced rank hidden markov models"
  Abstract: We introduce the Reduced-Rank Hidden Markov Model (RR-HMM), a generalization of HMMs that can model smooth state evolution as in Linear Dynamical Systems (LDSs) as well as non-log-concave predictive distributions as in continuous-observation HMMs. RR-HMMs assume an m-dimensional latent state and n discrete observations, with a transition matrix of rank k <= m. This implies the dynamics evolve in a k-dimensional subspace, while the shape of the set of predictive distributions is determined by m. Latent state belief is represented with a k-dimensional state vector and inference is carried out entirely in R^k, making RR-HMMs as computationally efficient as k-state HMMs yet more expressive. To learn RR-HMMs, we relax the assumptions of a recently proposed spectral learning algorithm for HMMs (Hsu, Kakade and Zhang 2009) and apply it to learn k-dimensional observable representations of rank-k RR-HMMs. The algorithm is consistent and free of local optima, and we extend its performance guarantees to cover the RR-HMM case. We show how this algorithm can be used in conjunction with a kernel density estimator to efficiently model high-dimensional multivariate continuous data. We also relax the assumption that single observations are sufficient to disambiguate state, and extend the algorithm accordingly. Experiments on synthetic data and a toy video, as well as on a difficult robot vision modeling problem, yield accurate models that compare favorably with standard alternatives in simulation quality and prediction capability.

Connected through hyperedge HE-1 (induced by source paper "singular value automata and approximate minimization"):
  with members:
    - "a canonical form for weighted automata and applications to approximate minimization"
    - "stability and complexity of minimising probabilistic automata"
    - "closing the learning planning loop with predictive state representations"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-2 (induced by source paper "sequential local learning for latent graphical models"):
  with members:
    - "unsupervised learning of noisy or bayesian networks"
    - "nonparametric estimation of multi view latent variable models"
    - "spectral experts for estimating mixtures of linear regressions"
    - "tensor decompositions for learning latent variable models"
    - "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 junction trees"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-3 (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"
    - "local loss optimization in operator models a new insight into spectral learning"
    - "a method of moments for mixture models and hidden markov models"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-4 (induced by source paper "bisimulation metrics for weighted automata"):
  with members:
    - "a canonical form for weighted automata and applications to approximate minimization"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-5 (induced by source paper "spectral learning of dynamic systems from nonequilibrium data"):
  with members:
    - "predictive state representations a new theory for modeling dynamical systems"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-6 (induced by source paper "learning parametric output hmms with two aliased states"):
  with members:
    - "minimal realization problems for hidden markov models"
    - "on learning parametric output hmms"
    - "a method of moments for mixture models and hidden markov models"
    - "polynomial learning of distribution families"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-7 (induced by source paper "a canonical form for weighted automata and applications to approximate minimization"):
  with members:
    - "stability and complexity of minimising probabilistic automata"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "closing the learning planning loop with predictive state representations"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-8 (induced by source paper "on learning parametric output hmms"):
  with members:
    - "local loss optimization in operator models a new insight into spectral learning"
    - "a method of moments for mixture models and hidden markov models"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"
    - "a spectral algorithm for learning hidden markov models"
    - "learning nonsingular phylogenies and hidden markov models"

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)