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: "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.

Connected through hyperedge HE-1 (induced by source paper "numerical linear algebra in the sliding window model"):
  with members:
    - "nearly optimal distinct elements and heavy hitters on sliding windows"
    - "lectures on randomized numerical linear algebra"
    - "submodular optimization over sliding windows"
    - "submodular maximization over sliding windows"
    - "online row sampling"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "frequent directions simple and deterministic matrix sketching"
    - "sparsified cholesky solvers for sdd linear systems"

Connected through hyperedge HE-2 (induced by source paper "spectrum approximation beyond fast matrix multiplication algorithms and hardness"):
  with members:
    - "fast estimation of approximate matrix ranks using spectral densities"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "embeddings of schatten norms with applications to data streams"
    - "improved stochastic trace estimation using mutually unbiased bases"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "solving ridge regression using sketched preconditioned svrg"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"

Connected through hyperedge HE-3 (induced by source paper "embeddings of schatten norms with applications to data streams"):
  with members:
    - "spectrum estimation from samples"
    - "on approximating functions of the singular values in a stream"
    - "streaming symmetric norms via measure concentration"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "principal component analysis and higher correlations for distributed data"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "a derandomized sparse johnson lindenstrauss transform"

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.DS(Data Structures and Algorithms)