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 "faster low rank approximation using adaptive gap based preconditioning"):
  Abstract: We propose a method for rank $k$ approximation to a given input matrix $X \in \mathbb{R}^{d \times n}$ which runs in time \[ \tilde{O} \left(d ~\cdot~ \min\left\{n + \tilde{sr}(X) \,G^{-2}_{k,p+1}\ ,\ n^{3/4}\, \tilde{sr}(X)^{1/4} \,G^{-1/2}_{k,p+1} \right\} ~\cdot~ \text{poly}(p)\right) ~, \] where $p>k$, $\tilde{sr}(X)$ is related to stable rank of $X$, and $G_{k,p+1} = \frac{\sigma_k-\sigma_p}{\sigma_k}$ is the multiplicative gap between the $k$-th and the $(p+1)$-th singular values of $X$. In particular, this yields a linear time algorithm if the gap is at least $1/\sqrt{n}$ and $k,p,\tilde{sr}(X)$ are constants.

Includes members:
  Member "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "smooth sensitivity based approach for differentially private principal component analysis")
  Member "convergence of stochastic gradient descent for pca":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exponentially convergent stochastic k pca without variance reduction")
  Member "fast stochastic algorithms for svd and pca convergence properties and convexity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "randomized block krylov methods for stronger and faster approximate singular value decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "homogeneous network embedding for massive graphs via reweighted personalized pagerank")
  Member "improved analyses of the randomized power method and block lanczos method":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "break the ceiling stronger multi scale deep graph convolutional networks")
  Member "sketching as a tool for numerical linear algebra":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximating spectral clustering via sampling a review")

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.IT(Information Theory)