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: "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
  Abstract: In this paper we show that very large mixtures of Gaussians are efficiently learnable in high dimension. More precisely, we prove that a mixture with known identical covariance matrices whose number of components is a polynomial of any fixed degree in the dimension n is polynomially learnable as long as a certain non-degeneracy condition on the means is satisfied. It turns out that this condition is generic in the sense of smoothed complexity, as soon as the dimensionality of the space is high enough. Moreover, we prove that no such condition can possibly exist in low dimension and the problem of learning the parameters is generically hard. In contrast, much of the existing work on Gaussian Mixtures relies on low-dimensional projections and thus hits an artificial barrier. Our main result on mixture recovery relies on a new "Poissonization"-based technique, which transforms a mixture of Gaussians to a linear map of a product distribution. The problem of learning this map can be efficiently solved using some recent results on tensor decompositions and Independent Component Analysis (ICA), thus giving an algorithm for recovering the mixture. In addition, we combine our low-dimensional hardness results for Gaussian mixtures with Poissonization to show how to embed difficult instances of low-dimensional Gaussian mixtures into the ICA setting, thus establishing exponential information-theoretic lower bounds for underdetermined ICA in low dimension. To the best of our knowledge, this is the first such result in the literature. In addition to contributing to the problem of Gaussian mixture learning, we believe that this work is among the first steps toward better understanding the rare phenomenon of the "blessing of dimensionality" in the computational aspects of statistical inference.

Connected through hyperedge HE-1 (induced by source paper "em algorithm is sample optimal for learning mixtures of well separated gaussians"):
  with members:
    - "em converges for a mixture of many linear regressions"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "sample efficient learning of mixtures"
    - "mixture models robustness and sum of squares proofs"
    - "on learning mixtures of well separated gaussians"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "robust estimators in high dimensions without the computational intractability"
    - "learning mixtures of gaussians in high dimensions"

Connected through hyperedge HE-2 (induced by source paper "high dimensional brain in a high dimensional world blessing of dimensionality"):
  with members:
    - "the unreasonable effectiveness of small neural ensembles in high dimensional brain"
    - "blessing of dimensionality at the edge"
    - "fast construction of correcting ensembles for legacy artificial intelligence systems algorithms and a case study"
    - "estimating the effective dimension of large biological datasets using fisher separability analysis"
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "stochastic separation theorems"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "effect of activation functions on the training of overparametrized neural nets"):
  with members:
    - "is it time to swish comparing deep learning activation functions across nlp tasks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the expressive power of deep polynomial neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"

Connected through hyperedge HE-6 (induced by source paper "fast construction of correcting ensembles for legacy artificial intelligence systems algorithms and a case study"):
  with members:
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "stochastic separation theorems"
    - "stochastic configuration networks fundamentals and algorithms"
    - "stochastic configuration networks ensemble with heterogeneous features for large scale data analytics"
    - "neurogenesis deep learning extending deep networks to accommodate new classes"
    - "deep stochastic configuration networks with universal approximation property"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-7 (induced by source paper "the unreasonable effectiveness of small neural ensembles in high dimensional brain"):
  with members:
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "augmented artificial intelligence"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "augmented artificial intelligence a conceptual framework"
    - "stochastic separation theorems"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "knowledge transfer between artificial intelligence systems"
    - "approximation with random bases"

Connected through hyperedge HE-8 (induced by source paper "smoothed analysis in unsupervised learning via decoupling"):
  with members:
    - "smoothed analysis of discrete tensor decomposition and assemblies of neurons"
    - "learning overcomplete hmms"
    - "local max cut in smoothed polynomial time"
    - "polynomial time tensor decompositions with sum of squares"
    - "learning mixtures of gaussians in high dimensions"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"
    - "minimal realization problems for hidden markov models"
    - "dictionary learning and tensor decomposition via the sum of squares method"

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)