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: "on sparse representations of linear operators and the approximation of matrix products"
  Abstract: Thus far, sparse representations have been exploited largely in the context of robustly estimating functions in a noisy environment from a few measurements. In this context, the existence of a basis in which the signal class under consideration is sparse is used to decrease the number of necessary measurements while controlling the approximation error. In this paper, we instead focus on applications in numerical analysis, by way of sparse representations of linear operators with the objective of minimizing the number of operations needed to perform basic operations (here, multiplication) on these operators. We represent a linear operator by a sum of rank-one operators, and show how a sparse representation that guarantees a low approximation error for the product can be obtained from analyzing an induced quadratic form. This construction in turn yields new algorithms for computing approximate matrix products.

Connected through hyperedge HE-1 (induced by source paper "when hashes met wedges a distributed algorithm for finding high similarity vectors"):
  with members:
    - "practical and optimal lsh for angular distance"
    - "diamond sampling for approximate maximum all pairs dot product mad search"
    - "the unified logging infrastructure for data analytics at twitter"
    - "complexity measures for map reduce and comparison to parallel computing"
    - "dimension independent similarity computation"
    - "compressed matrix multiplication"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-2 (induced by source paper "diamond sampling for approximate maximum all pairs dot product mad search"):
  with members:
    - "image based recommendations on styles and substitutes"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "compressed matrix multiplication"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"
    - "finding associations and computing similarity via biased pair sampling"

Connected through hyperedge HE-3 (induced by source paper "revisiting the nystrom method for improved large scale machine learning"):
  with members:
    - "matrix coherence and the nystrom method"
    - "improving cur matrix decomposition and the nystr o m approximation via adaptive sampling"
    - "improved bounds for the nystrom method with application to kernel classification"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "sharp analysis of low rank kernel matrix approximations"
    - "stochastic low rank kernel learning for regression"
    - "fast approximation of matrix coherence and statistical leverage"
    - "localization on low order eigenvectors of data matrices"

Connected through hyperedge HE-4 (induced by source paper "randomized algorithms for matrices and data"):
  with members:
    - "matrix coherence and the nystrom method"
    - "fast approximation of matrix coherence and statistical leverage"
    - "lsrn a parallel iterative solver for strongly over or under determined systems"
    - "distributed matrix completion and robust factorization"
    - "a sparse johnson lindenstrauss transform"
    - "row sampling for matrix algorithms via a non commutative bernstein bound"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"
    - "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)