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: "online learning for sparse pca in high dimensions exact dynamics and phase transitions"
  Abstract: We study the dynamics of an online algorithm for learning a sparse leading eigenvector from samples generated from a spiked covariance model. This algorithm combines the classical Oja's method for online PCA with an element-wise nonlinearity at each iteration to promote sparsity. In the high-dimensional limit, the joint empirical measure of the underlying sparse eigenvector and its estimate provided by the algorithm is shown to converge weakly to a deterministic, measure-valued process. This scaling limit is characterized as the unique solution of a nonlinear PDE, and it provides exact information regarding the asymptotic performance of the algorithm. For example, performance metrics such as the cosine similarity and the misclassification rate in sparse support recovery can be obtained by examining the limiting dynamics. A steady-state analysis of the nonlinear PDE also reveals an interesting phase transition phenomenon. Although our analysis is asymptotic in nature, numerical simulations show that the theoretical predictions are accurate for moderate signal dimensions.

Connected through hyperedge HE-1 (induced by source paper "detecting extrapolation with local ensembles"):
  with members:
    - "a study in rashomon curves and volumes a new perspective on generalization and model simplicity in machine learning"
    - "deep weighted averaging classifiers"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-2 (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"
    - "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"
    - "scaling limit exact and tractable analysis of online learning algorithms with applications to regularized regression and pca"
    - "stabilizing training of generative adversarial networks through regularization"
    - "gradient descent gan optimization is locally stable"

Connected through hyperedge HE-4 (induced by source paper "scaling limit exact and tractable analysis of online learning algorithms with applications to regularized regression and pca"):
  with members:
    - "hybrid approximate message passing"
    - "the scaling limit of high dimensional online independent component analysis"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"
    - "sharp time data tradeoffs for linear inverse problems"
    - "living on the edge phase transitions in convex programs with random data"
    - "expectation maximization gaussian mixture approximate message passing"
    - "sparse phase retrieval convex algorithms and limitations"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-5 (induced by source paper "the scaling limit of high dimensional online independent component analysis"):
  with members:
    - "scaling limit exact and tractable analysis of online learning algorithms with applications to regularized regression and pca"
    - "how to escape saddle points efficiently"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "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.IT(Information Theory)