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 "low rank approximation and decomposition of large matrices using error correcting codes"):
  Abstract: Low rank approximation is an important tool used in many applications of signal processing and machine learning. Recently, randomized sketching algorithms were proposed to effectively construct low rank approximations and obtain approximate singular value decompositions of large matrices. Similar ideas were used to solve least squares regression problems. In this paper, we show how matrices from error correcting codes can be used to find such low rank approximations and matrix decompositions, and extend the framework to linear least squares regression problems. The benefits of using these code matrices are the following. First, they are easy to generate and they reduce randomness significantly. Second, code matrices, with mild restrictions, satisfy the subspace embedding property, and have a better chance of preserving the geometry of an large subspace of vectors. Third, for parallel and distributed applications, code matrices have significant advantages over structured random matrices and Gaussian random matrices. Fourth, unlike Fourier or Hadamard transform matrices, which require sampling    $O(k\log k)$    columns for a rank-   $k$    approximation, the log factor is not necessary for certain types of code matrices. In particular,    $(1+ \epsilon)$    optimal Frobenius norm error can be achieved for a rank-   $k$    approximation with    $O(k/ \epsilon)$    samples. Fifth, fast multiplication is possible with structured code matrices, so fast approximations can be achieved for general dense input matrices. Sixth, for least squares regression problem    $\min \|Ax-b\|_{2}$    where    $A\in {\mathbb R} ^{n\times d}$   , the    $(1+ \epsilon)$    relative error approximation can be achieved with    $O(d/ \epsilon)$    samples, with high probability, when certain code matrices are used.

Includes members:
  Member "fast estimation of approximate matrix ranks using spectral densities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "find the dimension that counts fast dimension estimation and krylov pca")
  Member "randomized block krylov methods for stronger and faster approximate singular value decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "homogeneous network embedding for massive graphs via reweighted personalized pagerank")
  Member "restricted isometry property of random subdictionaries":
    also connected through other hyperedges:
      (none)
  Member "implementing randomized matrix algorithms in parallel and distributed environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "large scale constrained linear regression revisited faster algorithms via preconditioning")
  Member "sketching as a tool for numerical linear algebra":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "low rank matrix approximation in linear time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiplicative rank 1 approximation using length squared sampling")
  Member "low rank approximation and regression in input sparsity time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "on almost disjunct matrices for group testing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cross sender bit mixing coding")

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)