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: "rademacher chaos random eulerian graphs and the sparse johnson lindenstrauss transform"
  Abstract: The celebrated dimension reduction lemma of Johnson and Lindenstrauss has numerous computational and other applications. Due to its application in practice, speeding up the computation of a Johnson-Lindenstrauss style dimension reduction is an important question. Recently, Dasgupta, Kumar, and Sarlos (STOC 2010) constructed such a transform that uses a sparse matrix. This is motivated by the desire to speed up the computation when applied to sparse input vectors, a scenario that comes up in applications. The sparsity of their construction was further improved by Kane and Nelson (ArXiv 2010). #R##N#We improve the previous bound on the number of non-zero entries per column of Kane and Nelson from $O(1/\epsilon \log(1/\delta)\log(k/\delta))$ (where the target dimension is $k$, the distortion is $1\pm \epsilon$, and the failure probability is $\delta$) to $$ O\left({1\over\epsilon} \left({\log(1/\delta)\log\log\log(1/\delta) \over \log\log(1/\delta)}\right)^2\right). $$ #R##N#We also improve the amount of randomness needed to generate the matrix. Our results are obtained by connecting the moments of an order 2 Rademacher chaos to the combinatorial properties of random Eulerian multigraphs. Estimating the chance that a random multigraph is composed of a given number of node-disjoint Eulerian components leads to a new tail bound on the chaos. Our estimates may be of independent interest, and as this part of the argument is decoupled from the analysis of the coefficients of the chaos, we believe that our methods can be useful in the analysis of other chaoses.

Connected through hyperedge HE-1 (induced by source paper "fast binary embeddings with gaussian circulant matrices improved bounds"):
  with members:
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "binary embedding fundamental limits and fast algorithm"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "on binary embedding using circulant matrices"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "a sparse johnson lindenstrauss transform"

Connected through hyperedge HE-2 (induced by source paper "random projections for k means maintaining coresets beyond merge reduce"):
  with members:
    - "an algorithm for online k means clustering"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "deterministic feature selection for k means clustering"
    - "low rank approximation and regression in input sparsity time"
    - "randomized dimensionality reduction for k means clustering"
    - "a unified framework for approximating and clustering data"
    - "a sparse johnson lindenstrauss transform"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-3 (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-4 (induced by source paper "new constructions of rip matrices with fast multiplication and fewer rows"):
  with members:
    - "fast and rip optimal transforms"
    - "low rank approximation and regression in input sparsity time"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "stable manifold embeddings with structured random matrices"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "lower bounds for sparse recovery"

Connected through hyperedge HE-5 (induced by source paper "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"):
  with members:
    - "lower bounds for oblivious subspace embeddings"
    - "low rank approximation and regression in input sparsity time"
    - "iterative row sampling"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "fast matrix rank algorithms and applications"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "the fast cauchy transform and faster robust linear regression"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-6 (induced by source paper "recovering the optimal solution by dual random projection"):
  with members:
    - "a simple algorithm for semi supervised learning with improved generalization error bound"
    - "random projections for linear support vector machines"
    - "is margin preserved after random projection"
    - "relative error cur matrix decompositions"

Connected through hyperedge HE-7 (induced by source paper "sparser johnson lindenstrauss transforms"):
  with members:
    - "low rank approximation and regression in input sparsity time"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "a sparse johnson lindenstrauss transform"
    - "almost optimal explicit johnson lindenstrauss transformations"
    - "fast moment estimation in data streams in optimal space"
    - "a derandomized 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.DS(Data Structures and Algorithms)