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 fast convergence of incremental pca"
  Abstract: We consider a situation in which we see samples in $\mathbb{R}^d$ drawn i.i.d. from some distribution with mean zero and unknown covariance A. We wish to compute the top eigenvector of A in an incremental fashion - with an algorithm that maintains an estimate of the top eigenvector in O(d) space, and incrementally adjusts the estimate with each new data point that arrives. Two classical such schemes are due to Krasulina (1969) and Oja (1983). We give finite-sample convergence rates for both.

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 "batch stationary distribution estimation"):
  with members:
    - "adversarial learning of a sampler based on an unnormalized distribution"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "online factorization and partition of complex networks from random walks"
    - "using options and covariance testing for long horizon off policy policy evaluation"
    - "learning from conditional distributions via dual embeddings"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "exponentially convergent stochastic k pca without variance reduction"):
  with members:
    - "convergence of stochastic gradient descent for pca"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "optimal and adaptive algorithms for online boosting"
    - "global convergence of stochastic gradient descent for some non convex matrix problems"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "the noisy power method a meta algorithm with applications"
    - "matrix completion from a few entries"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "streaming batch eigenupdates for hardware neural networks"):
  with members:
    - "training lstm networks with resistive cross point devices"
    - "analog cmos based resistive processing unit for deep neural network training"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "the noisy power method a meta algorithm with applications"

Connected through hyperedge HE-5 (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"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"

Connected through hyperedge HE-6 (induced by source paper "stochastic approximation algorithms for principal component analysis"):
  with members:
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"

Connected through hyperedge HE-7 (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"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "learning the differential correlation matrix of a smooth function from point samples"

Connected through hyperedge HE-8 (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"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "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"

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)