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: "learning mixtures of gaussians using the k means algorithm"
  Abstract: One of the most popular algorithms for clustering in Euclidean space is the k-means algorithm; k-means is difficult to analyze mathematically, and few theoretical guarantees are known about it, particularly when the data is well-clustered. In this paper, we attempt to fill this gap in the literature by analyzing the behavior of k-means on well-clustered data. In particular, we study the case when each cluster is distributed as a different Gaussian – or, in other words, when the input comes from a mixture of Gaussians. We analyze three aspects of the k-means algorithm under this assumption. First, we show that when the input comes from a mixture of two spherical Gaussians, a variant of the 2-means algorithm successfully isolates the subspace containing the means of the mixture components. Second, we show an exact expression for the convergence of our variant of the 2-means algorithm, when the input is a very large number of samples from a mixture of spherical Gaussians. Our analysis does not require any lower bound on the separation between the mixture components. Finally, we study the sample requirement of k-means; for a mixture of 2 spherical Gaussians, we show an upper bound on the number of samples required by a variant of 2-means to get close to the true solution. The sample requirement grows with increasing dimensionality of the data, and decreasing separation between the means of the Gaussians. To match our upper bound, we show an information-theoretic lower bound on any algorithm that learns mixtures of two spherical Gaussians; our lower bound indicates that in the case when the overlap between the probability masses of the two distributions is small, the sample requirement of k-means is near-optimal.

Connected through hyperedge HE-1 (induced by source paper "on the analysis of em for truncated mixtures of two gaussians"):
  with members:
    - "settling the polynomial learnability of mixtures of gaussians"

Connected through hyperedge HE-2 (induced by source paper "convex relaxations of bregman divergence clustering"):
  with members:
    - "a hybrid algorithm for convex semidefinite optimization"
    - "k mle a fast algorithm for learning statistical mixture models"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "achieving approximate soft clustering in data streams"):
  with members:
    - "coresets for k means and k median clustering and their applications"
    - "optimal time bounds for approximate clustering"

Connected through hyperedge HE-4 (induced by source paper "spectral methods for learning multivariate latent tree structure"):
  with members:
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "reduced rank hidden markov models"
    - "a spectral algorithm for learning hidden markov models"
    - "learning nonsingular phylogenies and hidden markov models"

Connected through hyperedge HE-5 (induced by source paper "polynomial learning of distribution families"):
  with members:
    - "a two round variant of em for gaussian mixtures"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "learning gaussian mixtures with arbitrary separation"

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)