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: "scaling limit exact and tractable analysis of online learning algorithms with applications to regularized regression and pca"
  Abstract: We present a framework for analyzing the exact dynamics of a class of online learning algorithms in the high-dimensional scaling limit. Our results are applied to two concrete examples: online regularized linear regression and principal component analysis. As the ambient dimension tends to infinity, and with proper time scaling, we show that the time-varying joint empirical measures of the target feature vector and its estimates provided by the algorithms will converge weakly to a deterministic measured-valued process that can be characterized as the unique solution of a nonlinear PDE. Numerical solutions of this PDE can be efficiently obtained. These solutions lead to precise predictions of the performance of the algorithms, as many practical performance metrics are linear functionals of the joint empirical measures. In addition to characterizing the dynamic performance of online learning algorithms, our asymptotic analysis also provides useful insights. In particular, in the high-dimensional limit, and due to exchangeability, the original coupled dynamics associated with the algorithms will be asymptotically "decoupled", with each coordinate independently solving a 1-D effective minimization problem via stochastic gradient descent. Exploiting this insight for nonconvex optimization problems may prove an interesting line of future research.

Connected through hyperedge HE-1 (induced by source paper "a mean field theory for kernel alignment with random features in generative adversarial networks"):
  with members:
    - "improving mmd gan training with repulsive loss function"
    - "a solvable high dimensional model of gan"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "mode regularized generative adversarial networks"
    - "generative moment matching networks"
    - "algorithms for learning kernels based on centered alignment"

Connected through hyperedge HE-2 (induced by source paper "subspace estimation from incomplete observations a high dimensional analysis"):
  with members:
    - "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"
    - "convergence of stochastic gradient descent for pca"

Connected through hyperedge HE-3 (induced by source paper "a solvable high dimensional model of gan"):
  with members:
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "subspace estimation from incomplete observations a high dimensional analysis"
    - "which training methods for gans do actually converge"
    - "the numerics of gans"
    - "stabilizing training of generative adversarial networks through regularization"
    - "gradient descent gan optimization is locally stable"
    - "generalization and equilibrium in generative adversarial nets gans"

Connected through hyperedge HE-4 (induced by source paper "the scaling limit of high dimensional online independent component analysis"):
  with members:
    - "how to escape saddle points efficiently"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "online learning for sparse pca in high dimensions exact dynamics and phase transitions"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

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)