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: "spectrum estimation from samples"
  Abstract: We consider the problem of approximating the set of eigenvalues of the covariance matrix of a multivariate distribution (equivalently, the problem of approximating the "population spectrum"), given access to samples drawn from the distribution. The eigenvalues of the covariance of a distribution contain basic information about the distribution, including the presence or lack of structure in the distribution, the effective dimensionality of the distribution, and the applicability of higher-level machine learning and multivariate statistical tools. We consider this fundamental recovery problem in the regime where the number of samples is comparable, or even sublinear in the dimensionality of the distribution in question. First, we propose a theoretically optimal and computationally efficient algorithm for recovering the moments of the eigenvalues of the population covariance matrix. We then leverage this accurate moment recovery, via a Wasserstein distance argument, to show that the vector of eigenvalues can be accurately recovered. We provide finite--sample bounds on the expected error of the recovered eigenvalues, which imply that our estimator is asymptotically consistent as the dimensionality of the distribution and sample size tend towards infinity, even in the sublinear sample regime where the ratio of the sample size to the dimensionality tends to zero. In addition to our theoretical results, we show that our approach performs well in practice for a broad range of distributions and sample sizes.

Connected through hyperedge HE-1 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension"):
  with members:
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "approximating the spectrum of a graph"
    - "embeddings of schatten norms with applications to data streams"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"
    - "on approximating functions of the singular values in a stream"
    - "matrix norms in data streams faster multi pass and row order"
    - "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"

Connected through hyperedge HE-2 (induced by source paper "on the dimensionality of word embedding"):
  with members:
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "estimating learnability in the sublinear data regime"):
  with members:
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "minimax estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"
    - "new algorithms and lower bounds for monotonicity testing"
    - "the power of localization for efficiently learning linear separators with noise"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "active property testing"
    - "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"

Connected through hyperedge HE-4 (induced by source paper "moment based quantile sketches for efficient high cardinality aggregation queries"):
  with members:
    - "an efficient bandit algorithm for realtime multivariate optimization"
    - "macrobase prioritizing attention in fast data"
    - "an experimental study of distributed quantile estimation"
    - "blinkdb queries with bounded errors and bounded response times on very large data"
    - "a method of moments for mixture models and hidden markov models"
    - "polynomial learning of distribution families"
    - "medians and beyond new aggregation techniques for sensor networks"

Connected through hyperedge HE-5 (induced by source paper "spectrum approximation beyond fast matrix multiplication algorithms and hardness"):
  with members:
    - "fast estimation of approximate matrix ranks using spectral densities"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "embeddings of schatten norms with applications to data streams"
    - "improved stochastic trace estimation using mutually unbiased bases"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "sketches for matrix norms faster smaller and more general"
    - "solving ridge regression using sketched preconditioned svrg"
    - "faster eigenvector computation via shift and invert preconditioning"

Connected through hyperedge HE-6 (induced by source paper "drop dimensionality reduction optimization for time series"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "macrobase prioritizing attention in fast data"
    - "fma a dataset for music analysis"
    - "effortless data exploration with zenvisage an expressive and interactive visual analytics system"
    - "training region based object detectors with online hard example mining"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "global convergence of stochastic gradient descent for some non convex matrix problems"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"

Connected through hyperedge HE-7 (induced by source paper "embeddings of schatten norms with applications to data streams"):
  with members:
    - "sketches for matrix norms faster smaller and more general"
    - "on approximating functions of the singular values in a stream"
    - "streaming symmetric norms via measure concentration"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "principal component analysis and higher correlations for distributed data"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "a derandomized sparse johnson lindenstrauss transform"

Connected through hyperedge HE-8 (induced by source paper "approximating the spectrum of a graph"):
  with members:
    - "learning populations of parameters"
    - "sketching as a tool for numerical linear algebra"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "a quasi polynomial time partition oracle for graphs with an excluded minor"
    - "many sparse cuts via higher eigenvalues"

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)