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: "a two round variant of em for gaussian mixtures"
  Abstract: Given a set of possible models (e.g., Bayesian network structures) and a data sample, in the unsupervised model selection problem the task is to choose the most accurate model with respect to the domain joint probability distribution. In contrast to this, in supervised model selection it is a priori known that the chosen model will be used in the future for prediction tasks involving more ``focused' predictive distributions. Although focused predictive distributions can be produced from the joint probability distribution by marginalization, in practice the best model in the unsupervised sense does not necessarily perform well in supervised domains. In particular, the standard marginal likelihood score is a criterion for the unsupervised task, and, although frequently used for supervised model selection also, does not perform well in such tasks. In this paper we study the performance of the marginal likelihood score empirically in supervised Bayesian network selection tasks by using a large number of publicly available classification data sets, and compare the results to those obtained by alternative model selection criteria, including empirical crossvalidation methods, an approximation of a supervised marginal likelihood measure, and a supervised version of Dawids prequential(predictive sequential) principle.The results demonstrate that the marginal likelihood score does NOT perform well FOR supervised model selection, WHILE the best results are obtained BY using Dawids prequential r napproach.

Connected through hyperedge HE-1 (induced by source paper "provably efficient exploration for rl with unsupervised learning"):
  with members:
    - "agnostic q learning with function approximation in deterministic systems tight bounds on approximation error and sample complexity"
    - "provably efficient rl with rich observations via latent state decoding"
    - "provably efficient reinforcement learning with linear function approximation"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "sample optimal parametric q learning using linearly additive features"
    - "a theoretical analysis of deep q learning"
    - "efficient model free reinforcement learning in metric spaces"
    - "non asymptotic gap dependent regret bounds for tabular mdps"

Connected through hyperedge HE-2 (induced by source paper "private hypothesis selection"):
  with members:
    - "the optimal approximation factor in density estimation"
    - "differentially private algorithms for learning mixtures of separated gaussians"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "privately learning high dimensional distributions"
    - "locally private gaussian estimation"
    - "private pac learning implies finite littlestone dimension"
    - "learning sums of independent random variables with sparse collective support"
    - "inspectre privately estimating the unseen"

Connected through hyperedge HE-3 (induced by source paper "differentially private algorithms for learning mixtures of separated gaussians"):
  with members:
    - "private hypothesis selection"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "privately learning high dimensional distributions"
    - "differentially private k means with constant multiplicative error"
    - "inspectre privately estimating the unseen"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "finite sample differentially private confidence intervals"
    - "clustering algorithms for the centralized and local models"

Connected through hyperedge HE-4 (induced by source paper "efficiently learning structured distributions from untrusted batches"):
  with members:
    - "list decodable linear regression"
    - "list decodable learning via sum of squares"
    - "high dimensional estimation via sum of squares proofs"
    - "federated learning strategies for improving communication efficiency"
    - "being robust in high dimensions can be practical"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "learning discrete distributions from untrusted batches"
    - "learning populations of parameters"

Connected through hyperedge HE-5 (induced by source paper "coresets for gaussian mixture models of any shape"):
  with members:
    - "map inference via block coordinate frank wolfe algorithm"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "minimizing sum of non convex but piecewise log lipschitz functions using coresets"
    - "learning mixture of gaussians with streaming data"
    - "on the k means median cost function"
    - "secure search on the cloud via coresets and sketches"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "new frameworks for offline and streaming coreset constructions"

Connected through hyperedge HE-6 (induced by source paper "comparing em with gd in mixture models of two components"):
  with members:
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"

Connected through hyperedge HE-7 (induced by source paper "support localization and the fisher metric for off the grid sparse regularization"):
  with members:
    - "stable separation and super resolution of mixture models"
    - "robust recovery of stream of pulses using convex optimization"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "breaking the curse of dimensionality with convex neural networks"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"
    - "a probabilistic and ripless theory of compressed sensing"
    - "settling the polynomial learnability of mixtures of gaussians"

Connected through hyperedge HE-8 (induced by source paper "geometric methods for robust data analysis in high dimension"):
  with members:
    - "heavy tailed independent component analysis"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "fourier pca and robust tensor decomposition"
    - "blind signal separation in the presence of gaussian noise"
    - "tensor decompositions for learning latent variable models"
    - "efficient learning of simplices"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "provable ica with unknown gaussian noise and implications for gaussian mixtures and autoencoders"

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)