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: "a stochastic pca and svd algorithm with an exponential convergence rate"
  Abstract: We describe and analyze a simple algorithm for principal component analysis and singular value decomposition, VR-PCA, which uses computationally cheap stochastic iterations, yet converges exponentially fast to the optimal solution. In contrast, existing algorithms suffer either from slow convergence, or computationally intensive iterations whose runtime scales with the data size. The algorithm builds on a recent variance-reduced stochastic gradient technique, which was previously analyzed for strongly convex optimization, whereas here we apply it to an inherently non-convex problem, using a very different analysis.

Connected through hyperedge HE-1 (induced by source paper "learning a single neuron with gradient methods"):
  with members:
    - "fitting relus via sgd and quantized sgd"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "learning relus via gradient descent"

Connected through hyperedge HE-2 (induced by source paper "efficient fair principal component analysis"):
  with members:
    - "flexibly fair representation learning by disentanglement"
    - "fair dimensionality reduction and iterative rounding for sdps"
    - "downstream effects of affirmative action"
    - "convex formulations for fair principal component analysis"
    - "the price of fair pca one extra dimension"
    - "fairness under composition"
    - "a reductions approach to fair classification"
    - "eliminating latent discrimination train then mask"

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"
    - "the fast convergence of incremental 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"
    - "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 "winner take all computation in spiking neural networks"):
  with members:
    - "neuro ram unit with applications to similarity testing and compression in spiking neural networks"
    - "computational tradeoffs in biological neural networks self stabilizing winner take all networks"
    - "simple efficient and neural algorithms for sparse coding"
    - "uniform sampling for matrix approximation"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "random projections for k means clustering"

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"
    - "the fast convergence of incremental pca"

Connected through hyperedge HE-6 (induced by source paper "asvrg accelerated proximal svrg"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "vr sgd a simple stochastic variance reduction method for machine learning"
    - "accelerated variance reduced stochastic admm"
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "variance reduced and projection free stochastic optimization"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "mini batch semi stochastic gradient descent in the proximal setting"

Connected through hyperedge HE-7 (induced by source paper "vr sgd a simple stochastic variance reduction method for machine learning"):
  with members:
    - "accelerated variance reduced stochastic admm"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "nonconvex sparse learning via stochastic optimization with progressive variance reduction"
    - "variance reduced and projection free stochastic optimization"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "deep learning with elastic averaging sgd"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

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"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "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"

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)