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 note on element wise matrix sparsification via a matrix valued bernstein inequality"
  Abstract: Given a matrix A@?R^n^x^n, we present a simple, element-wise sparsification algorithm that zeroes out all sufficiently small elements of A and then retains some of the remaining elements with probabilities proportional to the square of their magnitudes. We analyze the approximation accuracy of the proposed algorithm using a recent, elegant non-commutative Bernstein inequality, and compare our bounds with all existing (to the best of our knowledge) element-wise matrix sparsification algorithms.

Connected through hyperedge HE-1 (induced by source paper "on dimension free tail inequalities for sums of random matrices and applications"):
  with members:
    - "matrix infinitely divisible series tail inequalities and applications in optimization"
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"
    - "circulant and toeplitz matrices in compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "convergence analysis of iterated best response for a trusted computation game"):
  with members:
    - "gaussian cheap talk game with quadratic cost functions when herding between strategic senders is a virtue"
    - "computing best response strategies in infinite games of incomplete information"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-3 (induced by source paper "improved practical matrix sketching with guarantees"):
  with members:
    - "frequent directions simple and deterministic matrix sketching"
    - "sketching as a tool for numerical linear algebra"
    - "provable deterministic leverage score sampling"
    - "continuous matrix approximation on distributed data"
    - "relative errors for deterministic low rank matrix approximations"
    - "low rank approximation and regression in input sparsity time"
    - "simple and deterministic matrix sketching"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"

Connected through hyperedge HE-4 (induced by source paper "dimensionality reduction of massive sparse datasets using coresets"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "online row sampling"
    - "frequent directions simple and deterministic matrix sketching"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "on the sensitivity of shape fitting problems"
    - "low rank approximation and regression in input sparsity time"
    - "simple and deterministic matrix sketching"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-5 (induced by source paper "recovering pca from hybrid l 1 l 2 sparse sampling of data elements"):
  with members:
    - "a note on randomized element wise matrix sparsification"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-6 (induced by source paper "subadditivity of matrix phi entropy and concentration of random matrices"):
  with members:
    - "blind deconvolution using convex programming"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "compressed sensing off the grid"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-7 (induced by source paper "near optimal entrywise sampling for data matrices"):
  with members:
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "simple and deterministic matrix sketching"):
  with members:
    - "low rank approximation and regression in input sparsity time"
    - "near optimal column based matrix reconstruction"
    - "a sparse johnson lindenstrauss transform"
    - "sparser johnson lindenstrauss transforms"
    - "feature hashing for large scale multitask learning"
    - "an improved approximation algorithm for the column subset selection problem"
    - "faster least squares approximation"
    - "relative error cur matrix decompositions"

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)