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: "k mle a fast algorithm for learning statistical mixture models"
  Abstract: We present a fast and generic algorithm, k-MLE, for learning statistical mixture models using maximum likelihood estimators. We prove theoretically that k-MLE is dually equivalent to a Bregman k-means for the case of mixtures of exponential families (e.g., Gaussian mixture models). k-MLE is used to initialize appropriately the expectation-maximization algorithm. We also show experimentally that k-MLE outperforms the EM technique with standard initialization by considering modeling color images using high-dimensional Gaussian mixture models.

Connected through hyperedge HE-1 (induced by source paper "on w mixtures finite convex combinations of prescribed component distributions"):
  with members:
    - "ensemble estimation of distributional functionals via k nearest neighbors"
    - "estimating mixture entropy with pairwise distances"
    - "on the chi square and higher order chi distances for approximating f divergences"
    - "multivariate f divergence estimation with confidence"
    - "chernoff information of exponential families"
    - "on renyi and tsallis entropies and divergences for exponential families"
    - "the burbea rao and bhattacharyya centroids"
    - "generalised pinsker inequalities"

Connected through hyperedge HE-2 (induced by source paper "optimal interval clustering application to bregman clustering and statistical mixture learning"):
  with members:
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-3 (induced by source paper "convex relaxations of bregman divergence clustering"):
  with members:
    - "a hybrid algorithm for convex semidefinite optimization"
    - "a convex relaxation for weakly supervised classifiers"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "a method of moments for mixture models and hidden markov models"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "learning mixtures of gaussians using the k means algorithm"

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)