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: "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
  Abstract: The online problem of computing the top eigenvector is fundamental to machine learning. In both adversarial and stochastic settings, previous results (such as matrix multiplicative weight update, follow the regularized leader, follow the compressed leader, block power method) either achieve optimal regret but run slow, or run fast at the expense of loosing a $\sqrt{d}$ factor in total regret where $d$ is the matrix dimension. #R##N#We propose a $\textit{follow-the-compressed-leader (FTCL)}$ framework which achieves optimal regret without sacrificing the running time. Our idea is to "compress" the matrix strategy to dimension 3 in the adversarial setting, or dimension 1 in the stochastic setting. These respectively resolve two open questions regarding the design of optimal and efficient algorithms for the online eigenvector problem.

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"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "positive semidefinite programming mixed parallel and width independent"):
  with members:
    - "a rank 1 sketch for matrix multiplicative weights"
    - "non convex matrix completion against a semi random adversary"
    - "high dimensional robust mean estimation in nearly linear time"
    - "efficient structured matrix recovery and nearly linear time algorithms for solving inverse symmetric m matrices"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "hardness results for structured linear systems"
    - "using optimization to obtain a width independent parallel simpler and faster positive sdp solver"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"

Connected through hyperedge HE-3 (induced by source paper "bandit principal component analysis"):
  with members:
    - "bilinear bandits with low rank structure"
    - "introduction to online convex optimization"
    - "more adaptive algorithms for adversarial bandits"
    - "a modular analysis of adaptive non convex optimization optimism composite objectives and variational bounds"
    - "sparsity variance and curvature in multi armed bandits"
    - "stochastic low rank bandits"
    - "corralling a band of bandit algorithms"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"

Connected through hyperedge HE-4 (induced by source paper "a rank 1 sketch for matrix multiplicative weights"):
  with members:
    - "introduction to online convex optimization"
    - "efficient structured matrix recovery and nearly linear time algorithms for solving inverse symmetric m matrices"
    - "stability of the lanczos method for matrix function approximation"
    - "geometric median in nearly linear time"
    - "faster and simpler width independent parallel algorithms for positive semidefinite programming"

Connected through hyperedge HE-5 (induced by source paper "an widetilde mathcal o m varepsilon 3 5 cost algorithm for semidefinite programs with diagonal constraints"):
  with members:
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "variance reduced and projection free stochastic optimization"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-6 (induced by source paper "a convergence theory for deep learning via over parameterization"):
  with members:
    - "non asymptotic identification of lti systems from a single trajectory"
    - "robust spectral filtering and anomaly detection"
    - "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"
    - "learning compact neural networks with regularization"
    - "on the convergence rate of training recurrent neural networks"
    - "learning long term dependencies via fourier recurrent units"

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"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "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 "katyusha x practical momentum method for stochastic sum of nonconvex optimization"):
  with members:
    - "how to make the gradients small stochastically"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "sdca without duality regularization and individual convexity"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"

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)