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: "sparsity lower bounds for dimensionality reducing maps"
  Abstract: We give near-tight lower bounds for the sparsity required in several dimensionality reducing linear maps. First, consider the JL lemma which states that for any set of n vectors in R there is a matrix A in R^{m x d} with m = O(eps^{-2}log n) such that mapping by A preserves pairwise Euclidean distances of these n vectors up to a 1 +/- eps factor. We show that there exists a set of n vectors such that any such matrix A with at most s non-zero entries per column must have s = Omega(eps^{-1}log n/log(1/eps)) as long as m < O(n/log(1/eps)). This bound improves the lower bound of Omega(min{eps^{-2}, eps^{-1}sqrt{log_m d}}) by [Dasgupta-Kumar-Sarlos, STOC 2010], which only held against the stronger property of distributional JL, and only against a certain restricted class of distributions. Meanwhile our lower bound is against the JL lemma itself, with no restrictions. Our lower bound matches the sparse Johnson-Lindenstrauss upper bound of [Kane-Nelson, SODA 2012] up to an O(log(1/eps)) factor. #R##N#Next, we show that any m x n matrix with the k-restricted isometry property (RIP) with constant distortion must have at least Omega(klog(n/k)) non-zeroes per column if the number of the rows is the optimal value m = O(klog (n/k)), and if k < n/polylog n. This improves the previous lower bound of Omega(min{k, n/m}) by [Chandar, 2010] and shows that for virtually all k it is impossible to have a sparse RIP matrix with an optimal number of rows. #R##N#Lastly, we show that any oblivious distribution over subspace embedding matrices with 1 non-zero per column and preserving all distances in a d dimensional-subspace up to a constant factor with constant probability must have at least Omega(d^2) rows. This matches one of the upper bounds in [Nelson-Nguyen, 2012] and shows the impossibility of obtaining the best of both of constructions in that work, namely 1 non-zero per column and \~O(d) rows.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving learning via deep net pruning"):
  with members:
    - "deep leakage from gradients"
    - "stronger l2 l2 compressed sensing without iterating"
    - "inverting deep generative models one layer at a time"
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "the state of sparsity in deep neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "federated learning strategies for improving communication efficiency"
    - "split learning for health distributed deep learning without sharing raw patient data"

Connected through hyperedge HE-2 (induced by source paper "efficient symmetric norm regression via linear sketching"):
  with members:
    - "on mean estimation for general norms with statistical queries"
    - "dimensionality reduction for tukey regression"
    - "tight bounds for ell_p oblivious subspace embeddings"
    - "subspace embedding and linear regression with orlicz norm"
    - "algorithmic theory of odes and sampling from well conditioned logconcave densities"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "approximate near neighbors for general symmetric norms"
    - "input sparsity and hardness for robust subspace approximation"

Connected through hyperedge HE-3 (induced by source paper "an improved lower bound for sparse reconstruction from subsampled hadamard matrices"):
  with members:
    - "the restricted isometry property of subsampled fourier matrices"
    - "discrete uncertainty principles and sparse signal processing"
    - "lower bounds for sparse recovery"

Connected through hyperedge HE-4 (induced by source paper "fully understanding the hashing trick"):
  with members:
    - "optimality of the johnson lindenstrauss lemma"
    - "hashing for statistics over k partitions"
    - "simple tabulation fast expanders double tabulation and high independence"
    - "random projections for linear support vector machines"
    - "a sparse johnson lindenstrauss transform"
    - "sparser johnson lindenstrauss transforms"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-5 (induced by source paper "sparsification of matrices and compressed sensing"):
  with members:
    - "deterministic constructions of binary measurement matrices from finite geometry"
    - "near optimal binary compressed sensing matrix"
    - "ldpc codes for compressed sensing"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "vanishingly sparse matrices and expander graphs with application to compressed sensing"
    - "restricted isometries for partial random circulant matrices"
    - "performance bounds for expander based compressed sensing in the presence of poisson noise"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-6 (induced by source paper "sketching as a tool for numerical linear algebra"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "optimal approximate matrix product in terms of stable rank"
    - "optimal cur matrix decompositions"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "uniform sampling for matrix approximation"
    - "single pass spectral sparsification in dynamic streams"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "subspace embeddings and ell_p regression using exponential random variables"

Connected through hyperedge HE-7 (induced by source paper "toward a unified theory of sparse dimensionality reduction in euclidean space"):
  with members:
    - "randomized sketches of convex programs with sharp guarantees"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "uniform recovery of fusion frame structured sparse signals"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "subspace embeddings and ell_p regression using exponential random variables"
    - "on model based rip 1 matrices"

Connected through hyperedge HE-8 (induced by source paper "lower bounds for oblivious subspace embeddings"):
  with members:
    - "random projections for linear support vector machines"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "almost optimal explicit johnson lindenstrauss transformations"

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)