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 and improved johnson lindenstrauss embeddings via the restricted isometry property"
  Abstract: Consider an m by N matrix Phi with the Restricted Isometry Property of order k and level delta, that is, the norm of any k-sparse vector in R^N is preserved to within a multiplicative factor of 1 +- delta under application of Phi. We show that by randomizing the column signs of such a matrix Phi, the resulting map with high probability embeds any fixed set of p = O(e^k) points in R^N into R^m without distorting the norm of any point in the set by more than a factor of 1 +- delta. Consequently, matrices with the Restricted Isometry Property and with randomized column signs provide optimal Johnson-Lindenstrauss embeddings up to logarithmic factors in N. In particular, our results improve the best known bounds on the necessary embedding dimension m for a wide class of structured random matrices; for partial Fourier and partial Hadamard matrices, we improve the recent bound m = O(delta^(-4) log(p) log^4(N)) appearing in Ailon and Liberty to m = O(delta^(-2) log(p) log^4(N)), which is optimal up to the logarithmic factors in N. Our results also have a direct application in the area of compressed sensing for redundant dictionaries.

Connected through hyperedge HE-1 (induced by source paper "finding the mode of a kernel density estimate"):
  with members:
    - "discrepancy coresets and sketches in machine learning"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "optimal terminal dimensionality reduction in euclidean space"
    - "improved coresets for kernel density estimates"
    - "coresets for kernel regression"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "estimating continuous distributions in bayesian classifiers"
    - "union of random minkowski sums and network vulnerability analysis"

Connected through hyperedge HE-2 (induced by source paper "unraveling the veil of subspace rip through near isometry on subspaces"):
  with members:
    - "compressed subspace learning based on canonical angle preserving property"
    - "rigorous restricted isometry property of low dimensional subspaces"
    - "restricted isometry property of gaussian random projection for finite set of subspaces"
    - "robust subspace clustering"
    - "low rank approximation and regression in input sparsity time"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "restricted isometries for partial random circulant matrices"

Connected through hyperedge HE-3 (induced by source paper "almost optimal tensor sketch"):
  with members:
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "optimal approximate matrix product in terms of stable rank"
    - "fastfood approximate kernel expansions in loglinear time"
    - "sparser johnson lindenstrauss transforms"
    - "feature hashing for large scale multitask learning"
    - "ams without 4 wise independence on product domains"

Connected through hyperedge HE-4 (induced by source paper "faster johnson lindenstrauss transforms via kronecker products"):
  with members:
    - "oblivious sketching of high degree polynomial kernels"
    - "a practical randomized cp tensor decomposition"
    - "sketching for kronecker product regression and p splines"
    - "optimality of the johnson lindenstrauss lemma"
    - "the restricted isometry property of subsampled fourier matrices"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"

Connected through hyperedge HE-5 (induced by source paper "johnson lindenstrauss property implies subspace restricted isometry property"):
  with members:
    - "rigorous restricted isometry property of low dimensional subspaces"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"

Connected through hyperedge HE-6 (induced by source paper "restricted isometry property under high correlations"):
  with members:
    - "a la carte embedding cheap but effective induction of semantic feature vectors"
    - "sketching as a tool for numerical linear algebra"
    - "exact recovery of sparsely used dictionaries"

Connected through hyperedge HE-7 (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-8 (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"
    - "new constructions of rip matrices with fast multiplication and fewer rows"
    - "randomized dimensionality reduction for k means clustering"
    - "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.IT(Information Theory)