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: "a sparse johnson lindenstrauss transform"
  Abstract: Dimension reduction is a key algorithmic tool with many applications including nearest-neighbor search, compressed sensing and linear algebra in the streaming model. In this work we obtain a {\em sparse} version of the fundamental tool in dimension reduction --- the Johnson--Lindenstrauss transform. Using hashing and local densification, we construct a sparse projection matrix with just $\tilde{O}(\frac{1}{\epsilon})$ non-zero entries per column. We also show a matching lower bound on the sparsity for a large class of projection matrices. Our bounds are somewhat surprising, given the known lower bounds of $\Omega(\frac{1}{\epsilon^2})$ both on the number of rows of any projection matrix and on the sparsity of projection matrices generated by natural constructions. #R##N#Using this, we achieve an $\tilde{O}(\frac{1}{\epsilon})$ update time per non-zero element for a $(1\pm\epsilon)$-approximate projection, thereby substantially outperforming the $\tilde{O}(\frac{1}{\epsilon^2})$ update time required by prior approaches. A variant of our method offers the same guarantees for sparse vectors, yet its $\tilde{O}(d)$ worst case running time matches the best approach of Ailon and Liberty.

Connected through hyperedge HE-1 (induced by source paper "the effectiveness of johnson lindenstrauss transform for high dimensional optimization with outliers"):
  with members:
    - "greedy strategy works for k center clustering with outliers and coreset construction"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "the non uniform k center problem"
    - "adversarial machine learning at scale"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "recovering the optimal solution by dual random projection"
    - "random projections for linear support vector machines"

Connected through hyperedge HE-2 (induced by source paper "oblivious sketching of high degree polynomial kernels"):
  with members:
    - "almost optimal tensor sketch"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "recursive sampling for the nystr om method"
    - "optimal approximate matrix product in terms of stable rank"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-3 (induced by source paper "similarity problems in high dimensions"):
  with members:
    - "approximate furthest neighbor with application to annulus query"
    - "distributed pcp theorems for hardness of approximation in p"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "set similarity search beyond minhash"
    - "distance sensitive bloom filters without false negatives"

Connected through hyperedge HE-4 (induced by source paper "random quadratic forms with dependence applications to restricted isometry and beyond"):
  with members:
    - "structured linear contextual bandits a sharp and geometric smoothed analysis"
    - "mitigating bias in adaptive data gathering via differential privacy"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "leveraged volume sampling for linear regression"
    - "subsampling for ridge regression via regularized volume sampling"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "structured compressed sensing from theory to applications"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-5 (induced by source paper "structured monte carlo sampling for nonisotropic distributions via determinantal point processes"):
  with members:
    - "when random search is not enough sample efficient and noise robust blackbox optimization of rl policies"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "guided evolutionary strategies augmenting random search with surrogate gradients"
    - "orthogonal random features"
    - "benchmarking deep reinforcement learning for continuous control"
    - "efficient sampling for k determinantal point processes"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (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"
    - "random projections for k means clustering"

Connected through hyperedge HE-7 (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"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "sparser johnson lindenstrauss transforms"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-8 (induced by source paper "tracking the ell_2 norm with constant update time"):
  with members:
    - "beating countsketch for heavy hitters in insertion streams"
    - "time lower bounds for nonadaptive turnstile streaming algorithms"
    - "tracking the frequency moments at all times"
    - "fast moment estimation in data streams in optimal space"
    - "sparser johnson lindenstrauss transforms"

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)