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 scaling limit of high dimensional online independent component analysis"
  Abstract: We analyze the dynamics of an online algorithm for independent component analysis in the high-dimensional scaling limit. As the ambient dimension tends to infinity, and with proper time scaling, we show that the time-varying joint empirical measure of the target feature vector and the estimates provided by the algorithm 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, which involves two spatial variables and one time variable, can be efficiently obtained. These solutions provide detailed information about the performance of the ICA algorithm, as many practical performance metrics are functionals of the joint empirical measures. Numerical simulations show that our asymptotic analysis is accurate even for moderate dimensions. In addition to providing a tool for understanding the performance of the algorithm, our PDE analysis also provides useful insight. In particular, in the high-dimensional limit, the original coupled dynamics associated with the algorithm will be asymptotically "decoupled", with each coordinate independently solving a 1-D effective minimization problem via stochastic gradient descent. Exploiting this insight to design new algorithms for achieving optimal trade-offs between computational and statistical efficiency may prove an interesting line of future research.

Connected through hyperedge HE-1 (induced by source paper "online learning a comprehensive survey"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "a survey of multi view representation learning"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "online deep learning learning deep neural networks on the fly"
    - "an online hierarchical algorithm for extreme clustering"
    - "efficient second order online learning by sketching"
    - "a framework of sparse online learning and its applications"
    - "second order quantile methods for experts and combinatorial games"

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"
    - "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"
    - "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"
    - "online learning for sparse pca in high dimensions exact dynamics and phase transitions"
    - "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"

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)