human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "subspace estimation from incomplete observations a high dimensional analysis"):
  Abstract: We present a high-dimensional analysis of three popular algorithms, namely, Oja's method, GROUSE, and PETRELS, for subspace estimation from streaming and highly incomplete observations. We show that, with proper time scaling, the time-varying principal angles between the true subspace and its estimates given by the algorithms converge weakly to deterministic processes when the ambient dimension   $n$   tends to infinity. Moreover, the limiting processes can be exactly characterized as the unique solutions of certain ordinary differential equations (ODEs). A finite sample bound is also given showing that the rate of convergence toward such limits is   $\mathcal {O}(1/\sqrt{n})$  . In addition to providing asymptotically exact predictions of the dynamic performance of the algorithms, our high-dimensional analysis yields several insights, including an asymptotic equivalence between Oja's method and GROUSE, and a precise scaling relationship linking the amount of missing data to the signal-to-noise ratio. By analyzing the solutions of the limiting ODEs, we also establish phase transition phenomena associated with the steady-state performance of these techniques.

Includes members:
  Member "scaling limit exact and tractable analysis of online learning algorithms with applications to regularized regression and pca":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a mean field theory for kernel alignment with random features in generative adversarial networks")
  Member "the scaling limit of high dimensional online independent component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online learning a comprehensive survey")
  Member "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "online learning for sparse pca in high dimensions exact dynamics and phase transitions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "detecting extrapolation with local ensembles")
  Member "provable efficient online matrix completion via non convex stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "understanding the downstream instability of word embeddings")
  Member "convergence of a grassmannian gradient descent algorithm for subspace estimation from undersampled data":
    also connected through other hyperedges:
      (none)
  Member "online matrix completion and online robust pca":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online high rank matrix completion")
  Member "global convergence of a grassmannian gradient descent algorithm for subspace estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")

Question: Please tell me which class the source paper 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)