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: "how to fake multiply by a gaussian matrix"
  Abstract: Have you ever wanted to multiply an $n \times d$ matrix $X$, with $n \gg d$, on the left by an $m \times n$ matrix $\tilde G$ of i.i.d. Gaussian random variables, but could not afford to do it because it was too slow? In this work we propose a new randomized $m \times n$ matrix $T$, for which one can compute $T \cdot X$ in only $O(\text{nnz}(X)) + \tilde O(m^{1.5} \cdot d^{3})$ time, for which the total variation distance between the distributions $T \cdot X$ and $\tilde G \cdot X$ is as small as desired, i.e., less than any positive constant. Here $\text{nnz}(X)$ denotes the number of non-zero entries of $X$. Assuming $\text{nnz}(X) \gg m^{1.5} \cdot d^{3}$, this is a significant savings over the na\"ive $O(\text{nnz}(X) m)$ time to compute $\tilde G \cdot X$. Moreover, since the total variation distance is small, we can provably use $T \cdot X$ in place of $\tilde G \cdot X$ in any application and have the same guarantees as if we were using $\tilde G \cdot X$, up to a small positive constant in error probability. We apply this transform to nonnegative matrix factorization (NMF) and support vector machines (SVM).

Connected through hyperedge HE-1 (induced by source paper "is input sparsity time possible for kernel low rank approximation"):
  with members:
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "low rank approximation and regression in input sparsity time"
    - "faster algorithms for rectangular matrix multiplication"
    - "explicit approximations of the gaussian kernel"

Connected through hyperedge HE-2 (induced by source paper "sketches for matrix norms faster smaller and more general"):
  with members:
    - "embeddings of schatten norms with applications to data streams"
    - "spectrum estimation from samples"
    - "on approximating functions of the singular values in a stream"
    - "distributed estimation of generalized matrix rank efficient algorithms and lower bounds"
    - "optimal approximate matrix product in terms of stable rank"
    - "streaming symmetric norms via measure concentration"
    - "sketching cuts in graphs and hypergraphs"
    - "sketching and embedding are equivalent for norms"

Connected through hyperedge HE-3 (induced by source paper "sketching for sequential change point detection"):
  with members:
    - "information recovery from pairwise measurements a shannon theoretic approach"
    - "change detection with compressive measurements"
    - "sketching as a tool for numerical linear algebra"
    - "local convergence of an algorithm for subspace identification from partial data"
    - "quickest search over multiple sequences with mixed observations"
    - "compressed hypothesis testing to mix or not to mix"

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)