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 "sketches for matrix norms faster smaller and more general"):
  Abstract: We design new sketching algorithms for unitarily invariant matrix norms, including the Schatten $p$-norms~$\|{\cdot}\|_{S_p}$, and obtain, as a by-product, streaming algorithms that approximate the norm of a matrix $A$ presented as a turnstile data stream. The primary advantage of our streaming algorithms is that they are simpler and faster than previous algorithms, while requiring the same or less storage. Our three main results are a faster sketch for estimating $\|{A}\|_{S_p}$, a smaller-space $O(1)$-pass sketch for $\|{A}\|_{S_p}$, and more general sketching technique that yields sublinear-space approximations for a wide class of matrix norms. These improvements are powered by dimensionality reduction techniques that are modern incarnations of the Johnson-Lindenstrauss Lemma~\cite{JL84}. When $p\geq 2$ is even or $A$ is PSD, our fast one-pass algorithm approximates $\|{A}\|_{S_p}$ in optimal, $n^{2-4/p}$, space with $O(1)$ update time and $o(n^{2.4(1-2/p)})$ time to extract the approximation from the sketch, while the $\lceil p/2\rceil$-pass algorithm is built on a smaller sketch of size $n^{1-1/(p-1)}$ with $O(1)$ update time and $n^{1-1/(p-1)}$ query time. Finally, for a PSD matrix $A$ and a unitarily invariant norm $l(\cdot)$, we prove that one can obtain an approximation to $l(A)$ from a sketch $GAH^T$ where $G$ and $H$ are independent Oblivious Subspace Embeddings and the dimension of the sketch is polynomial in the intrinsic dimension of $A$. The intrinsic dimension of a matrix is a robust version of the rank that is equal to the ratio $\sum_i\sigma_i/\sigma_1$. It is small, e.g., for models in machine learning which consist of a low rank matrix plus noise. Naturally, this leads to much smaller sketches for many norms.

Includes members:
  Member "embeddings of schatten norms with applications to data streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")
  Member "how to fake multiply by a gaussian matrix":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "is input sparsity time possible for kernel low rank approximation")
  Member "spectrum estimation from samples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")
  Member "on approximating functions of the singular values in a stream":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")
  Member "distributed estimation of generalized matrix rank efficient algorithms and lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")
  Member "optimal approximate matrix product in terms of stable rank":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "speeding up linear programming using randomized linear algebra")
  Member "streaming symmetric norms via measure concentration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "sketching cuts in graphs and hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")

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.DS(Data Structures and Algorithms)