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 learning parametric output hmms"
  Abstract: We present a novel approach for learning an HMM whose outputs are distributed according to a parametric family. This is done by {\em decoupling} the learning task into two steps: first estimating the output parameters, and then estimating the hidden states transition probabilities. The first step is accomplished by fitting a mixture model to the output stationary distribution. Given the parameters of this mixture model, the second step is formulated as the solution of an easily solvable convex quadratic program. We provide an error analysis for the estimated transition probabilities and show they are robust to small perturbations in the estimates of the mixture parameters. Finally, we support our analysis with some encouraging empirical results.

Connected through hyperedge HE-1 (induced by source paper "divergence based motivation for online em and combining hidden variable models"):
  with members:
    - "an open source c implementation of multi threaded gaussian mixture models k means and expectation maximisation"

Connected through hyperedge HE-2 (induced by source paper "state aggregation learning from markov transition data"):
  with members:
    - "sample optimal parametric q learning with linear transition models"
    - "on learning markov chains"
    - "scalable bilinear pi learning using state and action features"
    - "learning arbitrary statistical mixtures of discrete distributions"
    - "learning mixtures of arbitrary distributions over large discrete domains"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-3 (induced by source paper "asymptotically efficient identification of known sensor hidden markov models"):
  with members:
    - "forward mode automatic differentiation in julia"
    - "tensor decompositions for learning latent variable models"
    - "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 "reinforcement learning of pomdps using spectral methods"):
  with members:
    - "a pac rl algorithm for episodic pomdps"
    - "online learning and optimization of markov jump affine models"
    - "selecting near optimal approximate state representations in reinforcement learning"
    - "efficient learning and planning with compressed predictive states"
    - "nonparametric estimation of multi view latent variable models"
    - "pegasus a policy search method for large mdps and pomdps"
    - "tensor decompositions for learning latent variable models"
    - "a method of moments for mixture models and hidden markov models"

Connected through hyperedge HE-5 (induced by source paper "reinforcement learning of pomdp s using spectral methods"):
  with members:
    - "a pac rl algorithm for episodic pomdps"
    - "contextual mdps for pac reinforcement learning with rich observations"
    - "online learning and optimization of markov jump affine models"
    - "selecting near optimal approximate state representations in reinforcement learning"
    - "nonparametric estimation of multi view latent variable models"
    - "pegasus a policy search method for large mdps and pomdps"
    - "tensor decompositions for learning latent variable models"
    - "a method of moments for mixture models and hidden markov models"

Connected through hyperedge HE-6 (induced by source paper "sdp relaxation with randomized rounding for energy disaggregation"):
  with members:
    - "neural nilm deep neural networks applied to energy disaggregation"
    - "implementing spectral methods for hidden markov models with real valued emissions"
    - "a method of moments for mixture models and hidden markov models"

Connected through hyperedge HE-7 (induced by source paper "learning parametric output hmms with two aliased states"):
  with members:
    - "minimal realization problems for hidden markov models"
    - "a method of moments for mixture models and hidden markov models"
    - "polynomial learning of distribution families"
    - "reduced rank hidden markov models"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-8 (induced by source paper "implementing spectral methods for hidden markov models with real valued emissions"):
  with members:
    - "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)