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: "fast and rip optimal transforms"
  Abstract: We study constructions of $k \times n$ matrices $A$ that both (1) satisfy the restricted isometry property (RIP) at sparsity $s$ with optimal parameters, and (2) are efficient in the sense that only $O(n\log n)$ operations are required to compute $Ax$ given a vector $x$. Our construction is based on repeated application of independent transformations of the form $DH$, where $H$ is a Hadamard or Fourier transform and $D$ is a diagonal matrix with random $\{+1,-1\}$ elements on the diagonal, followed by any $k \times n$ matrix of orthonormal rows (e.g.\ selection of $k$ coordinates). We provide guarantees (1) and (2) for a larger regime of parameters for which such constructions were previously unknown. Additionally, our construction does not suffer from the extra poly-logarithmic factor multiplying the number of observations $k$ as a function of the sparsity $s$, as present in the currently best known RIP estimates for partial random Fourier matrices and other classes of structured random matrices.

Connected through hyperedge HE-1 (induced by source paper "fast fixed dimension l2 subspace embeddings of arbitrary accuracy with application to l1 and l2 tasks"):
  with members:
    - "quantile regression for large scale applications"
    - "subspace embeddings and ell_p regression using exponential random variables"
    - "low rank approximation and regression in input sparsity time"
    - "iterative row sampling"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "the fast cauchy transform and faster robust linear regression"
    - "fast approximation of matrix coherence and statistical leverage"
    - "row sampling for matrix algorithms via a non commutative bernstein bound"

Connected through hyperedge HE-2 (induced by source paper "improved bounds for sparse recovery from subsampled random convolutions"):
  with members:
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "the restricted isometry property of subsampled fourier matrices"
    - "the restricted isometry property for time frequency structured random matrices"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"
    - "restricted isometries for partial random circulant matrices"
    - "circulant and toeplitz matrices in compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "randomized algorithms for distributed computation of principal component analysis and singular value decomposition"):
  with members:
    - "direct qr factorizations for tall and skinny matrices in mapreduce architectures"

Connected through hyperedge HE-4 (induced by source paper "isometric sketching of any set via the restricted isometry property"):
  with members:
    - "the restricted isometry property of subsampled fourier matrices"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "sharp time data tradeoffs for linear inverse problems"
    - "learning without concentration"
    - "randomized sketches of convex programs with sharp guarantees"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"

Connected through hyperedge HE-5 (induced by source paper "practical and optimal lsh for angular distance"):
  with members:
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "optimal data dependent hashing for approximate near neighbors"
    - "beyond locality sensitive hashing"
    - "bucketing coding and information theory for the statistical high dimensional nearest neighbor problem"
    - "feature hashing for large scale multitask learning"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"
    - "lower bounds on locality sensitive hashing"

Connected through hyperedge HE-6 (induced by source paper "binary embedding fundamental limits and fast algorithm"):
  with members:
    - "fast binary embeddings with gaussian circulant matrices improved bounds"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "new constructions of rip matrices with fast multiplication and fewer rows"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-7 (induced by source paper "new constructions of rip matrices with fast multiplication and fewer rows"):
  with members:
    - "low rank approximation and regression in input sparsity time"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "stable manifold embeddings with structured random matrices"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "lower bounds for sparse recovery"
    - "a 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.NA(Numerical Analysis)