human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "an information theoretic analysis of hard and soft assignment methods for clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic k means clustering via nonlinear programming")
  Member "approximate bregman near neighbors in sublinear time beyond the triangle inequality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locality sensitive hashing for f divergences mutual information loss and beyond")
  Member "settling the polynomial learnability of mixtures of gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "when can wasserstein gans minimize wasserstein distance")
  Member "polynomial learning of distribution families":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "private hypothesis selection")
  Member "statistical exponential families a digest with flash cards":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the jensen shannon symmetrization of distances relying on abstract means")
  Member "k means requires exponentially many iterations even in the plane":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analysis of ward s method")
  Member "inference and evaluation of the multinomial mixture model for text clustering":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)