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: "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
  Abstract: This work provides improved guarantees for streaming principle component analysis (PCA). Given $A_1, \ldots, A_n\in \mathbb{R}^{d\times d}$ sampled independently from distributions satisfying $\mathbb{E}[A_i] = \Sigma$ for $\Sigma \succeq \mathbf{0}$, this work provides an $O(d)$-space linear-time single-pass streaming algorithm for estimating the top eigenvector of $\Sigma$. The algorithm nearly matches (and in certain cases improves upon) the accuracy obtained by the standard batch method that computes top eigenvector of the empirical covariance $\frac{1}{n} \sum_{i \in [n]} A_i$ as analyzed by the matrix Bernstein inequality. Moreover, to achieve constant accuracy, our algorithm improves upon the best previous known sample complexities of streaming algorithms by either a multiplicative factor of $O(d)$ or $1/\mathrm{gap}$ where $\mathrm{gap}$ is the relative distance between the top two eigenvalues of $\Sigma$. #R##N#These results are achieved through a novel analysis of the classic Oja's algorithm, one of the oldest and most popular algorithms for streaming PCA. In particular, this work shows that simply picking a random initial point $w_0$ and applying the update rule $w_{i + 1} = w_i + \eta_i A_i w_i$ suffices to accurately estimate the top eigenvector, with a suitable choice of $\eta_i$. We believe our result sheds light on how to efficiently perform streaming PCA both in theory and in practice and we hope that our analysis may serve as the basis for analyzing many variants and extensions of streaming PCA.

Connected through hyperedge HE-1 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis"):
  with members:
    - "bandit principal component analysis"
    - "exponentially convergent stochastic k pca without variance reduction"
    - "an implicit form of krasulina s k pca update without the orthonormality constraint"
    - "on the regret minimization of nonconvex online gradient ascent for online pca"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "how to make the gradients small stochastically even faster convex and nonconvex sgd"
    - "communication efficient algorithms for distributed stochastic principal component analysis"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"

Connected through hyperedge HE-2 (induced by source paper "powersgd practical low rank gradient compression for distributed optimization"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "detecting memorization in relu networks"
    - "gradiveq vector quantization for bandwidth efficient gradient aggregation in distributed cnn training"
    - "stronger generalization bounds for deep nets via a compression approach"

Connected through hyperedge HE-3 (induced by source paper "learning functions over sets via permutation adversarial networks"):
  with members:
    - "learning representations of sets through optimized permutations"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "deeppermnet visual permutation learning"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "semi supervised classification with graph convolutional networks"
    - "deepsetnet predicting sets with deep neural networks"
    - "diffusion convolutional neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "an implicit form of krasulina s k pca update without the orthonormality constraint"):
  with members:
    - "exponentially convergent stochastic k pca without variance reduction"
    - "divergence based motivation for online em and combining hidden variable models"
    - "the fast convergence of incremental pca"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"

Connected through hyperedge HE-5 (induced by source paper "moses a streaming algorithm for linear dimensionality reduction"):
  with members:
    - "robust frequent directions with application in online learning"
    - "what to expect when you are expecting on the grassmannian"
    - "fixed rank approximation of a positive semidefinite matrix from streaming data"
    - "streaming principal component analysis from incomplete data"
    - "weighted matrix completion and recovery with prior subspace information"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "learning the differential correlation matrix of a smooth function from point samples"
    - "global convergence of a grassmannian gradient descent algorithm for subspace estimation"

Connected through hyperedge HE-6 (induced by source paper "subspace estimation from incomplete observations a high dimensional analysis"):
  with members:
    - "scaling limit exact and tractable analysis of online learning algorithms with applications to regularized regression and pca"
    - "the scaling limit of high dimensional online independent component analysis"
    - "online learning for sparse pca in high dimensions exact dynamics and phase transitions"
    - "provable efficient online matrix completion via non convex stochastic gradient descent"
    - "convergence of a grassmannian gradient descent algorithm for subspace estimation from undersampled data"
    - "online matrix completion and online robust pca"
    - "global convergence of a grassmannian gradient descent algorithm for subspace estimation"
    - "convergence of stochastic gradient descent for pca"

Connected through hyperedge HE-7 (induced by source paper "on the regret minimization of nonconvex online gradient ascent for online pca"):
  with members:
    - "introduction to online convex optimization"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "matrix completion has no spurious local minimum"
    - "provable efficient online matrix completion via non convex stochastic gradient descent"
    - "convergence of stochastic gradient descent for pca"
    - "the fast convergence of incremental pca"
    - "provable bounds for learning some deep representations"
    - "near optimal algorithms for online matrix prediction"

Connected through hyperedge HE-8 (induced by source paper "dimensionality reduction for stationary time series via stochastic nonconvex optimization"):
  with members:
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "symmetry saddle points and global geometry of nonconvex matrix factorization"
    - "complete dictionary recovery over the sphere"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

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)