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: "sparser johnson lindenstrauss transforms"
  Abstract: We give two different and simple constructions for dimensionality reduction in $\ell_2$ via linear mappings that are sparse: only an $O(\varepsilon)$-fraction of entries in each column of our embedding matrices are non-zero to achieve distortion $1+\varepsilon$ with high probability, while still achieving the asymptotically optimal number of rows. These are the first constructions to provide subconstant sparsity for all values of parameters, improving upon previous works of Achlioptas (JCSS 2003) and Dasgupta, Kumar, and Sarl\'{o}s (STOC 2010). Such distributions can be used to speed up applications where $\ell_2$ dimensionality reduction is used.

Connected through hyperedge HE-1 (induced by source paper "sketching transformed matrices with applications to natural language processing"):
  with members:
    - "solving tall dense linear programs in nearly linear time"
    - "tight bounds for the subspace sketch problem with applications"
    - "optimal sketching for kronecker product regression and low rank approximation"
    - "a near optimal algorithm for approximating the john ellipsoid"
    - "regularized weighted low rank approximation"
    - "robust and sample optimal algorithms for psd low rank approximation"
    - "dimensionality reduction for tukey regression"
    - "total least squares regression in input sparsity time"

Connected through hyperedge HE-2 (induced by source paper "solving tall dense linear programs in nearly linear time"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "dynamic matrix inverse improved algorithms and matching conditional lower bounds"
    - "dynamic streaming spectral sparsification in nearly linear time and space"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "a deterministic linear program solver in current matrix multiplication time"
    - "solving linear programs in the current matrix multiplication time"
    - "relative error tensor low rank approximation"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"

Connected through hyperedge HE-3 (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-4 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications"):
  with members:
    - "phase retrieval under a generative prior"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "why are deep nets reversible a simple theory with implications for training"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction with subgaussian matrices a unified theory"

Connected through hyperedge HE-5 (induced by source paper "stable sparse subspace embedding for dimensionality reduction"):
  with members:
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "recovering the optimal solution by dual random projection"
    - "random projections for linear support vector machines"
    - "low rank approximation and regression in input sparsity time"
    - "random projections for k means clustering"
    - "bounded independence fools degree 2 threshold functions"

Connected through hyperedge HE-6 (induced by source paper "approximating text to pattern distance via dimensionality reduction"):
  with members:
    - "l p pattern matching in a stream"
    - "approximating approximate pattern matching"
    - "the streaming k mismatch problem"
    - "approximate hamming distance in a stream"

Connected through hyperedge HE-7 (induced by source paper "optimal sketching for kronecker product regression and low rank approximation"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "almost optimal tensor sketch"
    - "oblivious sketching of high degree polynomial kernels"
    - "a deterministic linear program solver in current matrix multiplication time"
    - "tight bounds for ell_p oblivious subspace embeddings"
    - "solving linear programs in the current matrix multiplication time"
    - "towards a zero one law for entrywise low rank approximation"
    - "sublinear time low rank approximation of distance matrices"

Connected through hyperedge HE-8 (induced by source paper "towards optimal moment estimation in streaming and distributed models"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "weighted reservoir sampling from distributed streams"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "distributed statistical estimation of matrix products with applications"
    - "revisiting frequency moment estimation in random order streams"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "continuous monitoring of ell_p norms in data streams"
    - "communication optimal distributed clustering"

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)