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: "dirichlet process mixtures of generalized mallows models"
  Abstract: We present a Dirichlet process mixture model over discrete incomplete rankings and study two Gibbs sampling inference techniques for estimating posterior clusterings. The first approach uses a slice sampling subcomponent for estimating cluster parameters. The second approach marginalizes out several cluster parameters by taking advantage of approximations to the conditional posteriors. We empirically demonstrate (1) the effectiveness of this approximation for improving convergence, (2) the benefits of the Dirichlet process model over alternative clustering techniques for ranked data, and (3) the applicability of the approach to exploring large realworld ranking datasets.

Connected through hyperedge HE-1 (induced by source paper "efficiently learning mixtures of mallows models"):
  with members:
    - "learning mixtures of plackett luce models"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "learning mixtures of ranking models"
    - "tensor decompositions for learning latent variable models"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"
    - "noisy sorting without resampling"

Connected through hyperedge HE-2 (induced by source paper "learning mixtures of ranking models"):
  with members:
    - "smoothed analysis of tensor decompositions"
    - "uniqueness of tensor decompositions with applications to polynomial identifiability"
    - "fourier pca and robust tensor decomposition"
    - "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"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "sorting from noisy information"

Connected through hyperedge HE-3 (induced by source paper "bayesian vote manipulation optimal strategies and impact on welfare"):
  with members:
    - "dominating manipulations in voting with partial information"
    - "complexity of and algorithms for borda manipulation"
    - "an empirical study of the manipulability of single transferable voting"
    - "where are the really hard manipulation problems the phase transition in manipulating the veto rule"

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)