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: "new constructions of rip matrices with fast multiplication and fewer rows"
  Abstract: In compressed sensing, the "restricted isometry property" (RIP) is a sufficient condition for the efficient reconstruction of a nearly k-sparse vector x in C^d from m linear measurements Phi x. It is desirable for m to be small, and for Phi to support fast matrix-vector multiplication. In this work, we give a randomized construction of RIP matrices Phi in C^{m x d}, preserving the L_2 norms of all k-sparse vectors with distortion 1+eps, where the matrix-vector multiply Phi x can be computed in nearly linear time. The number of rows m is on the order of eps^{-2}klog dlog^2(klog d). Previous analyses of constructions of RIP matrices supporting fast matrix-vector multiplies, such as the sampled discrete Fourier matrix, required m to be larger by roughly a log k factor. #R##N#Supporting fast matrix-vector multiplication is useful for iterative recovery algorithms which repeatedly multiply by Phi or Phi^*. Furthermore, our construction, together with a connection between RIP matrices and the Johnson-Lindenstrauss lemma in [Krahmer-Ward, SIAM. J. Math. Anal. 2011], implies fast Johnson-Lindenstrauss embeddings with asymptotically fewer rows than previously known. #R##N#Our approach is a simple twist on previous constructions. Rather than choosing the rows for the embedding matrix to be rows sampled from some larger structured matrix (such as the discrete Fourier transform or a random circulant matrix), we instead choose each row of the embedding matrix to be a linear combination of a small number of rows of the original matrix, with random sign flips as coefficients. The main tool in our analysis is a recent bound for the supremum of certain types of Rademacher chaos processes in [Krahmer-Mendelson-Rauhut, arXiv:1207.0235].

Connected through hyperedge HE-1 (induced by source paper "race sub linear memory sketches for approximate near neighbor search on streaming data"):
  with members:
    - "approximate nearest neighbors in limited space"
    - "hashing based estimators for kernel density in high dimensions"
    - "billion scale similarity search with gpus"
    - "when hashes met wedges a distributed algorithm for finding high similarity vectors"
    - "near optimal euclidean metric compression"
    - "practical and optimal lsh for angular distance"
    - "locality sensitive hashing without false negatives"
    - "beyond locality sensitive hashing"

Connected through hyperedge HE-2 (induced by source paper "mc ista net adaptive measurement and initialization and channel attention optimization inspired neural network for compressive sensing"):
  with members:
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "image super resolution using very deep residual channel attention networks"
    - "ensemble one dimensional convolution neural networks for skeleton based action recognition"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "learning deep cnn denoiser prior for image restoration"
    - "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"
    - "denoising based vector approximate message passing"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "fast binary embeddings and quantized compressed sensing with structured matrices"):
  with members:
    - "quantized compressed sensing for partial random circulant matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "fast binary embeddings with gaussian circulant matrices improved bounds"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "near optimal sample complexity bounds for circulant binary embedding"

Connected through hyperedge HE-4 (induced by source paper "performance of johnson lindenstrauss transform for k means and k medians clustering"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "optimality of the johnson lindenstrauss lemma"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "deterministic feature selection for k means clustering"
    - "randomized dimensionality reduction for k means clustering"
    - "a sparse johnson lindenstrauss transform"
    - "random projections for k means clustering"

Connected through hyperedge HE-5 (induced by source paper "random subsets of structured deterministic frames have manova spectra"):
  with members:
    - "analog coding of a source with erasures"
    - "the restricted isometry property of subsampled fourier matrices"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "the restricted isometry property for time frequency structured random matrices"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "the statistical restricted isometry property and the wigner semicircle distribution of incoherent dictionaries"
    - "asymptotic behaviour of random vandermonde matrices with entries on the unit circle"

Connected through hyperedge HE-6 (induced by source paper "sparse recovery in wigner d basis expansion"):
  with members:
    - "discrete uncertainty principles and sparse signal processing"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"

Connected through hyperedge HE-7 (induced by source paper "optimal approximate matrix product in terms of stable rank"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "improved distributed principal component analysis"
    - "uniform sampling for matrix approximation"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "lower bounds for oblivious subspace embeddings"
    - "low rank approximation and regression in input sparsity time"
    - "new probabilistic bounds on eigenvalues and eigenvectors of random kernel matrices"

Connected through hyperedge HE-8 (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"
    - "fast and rip optimal transforms"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"

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)