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: "algorithmic and statistical perspectives on large scale data analysis"
  Abstract: In recent years, ideas from statistics and scientific computing have begun to interact in increasingly sophisticated and fruitful ways with ideas from computer science and the theory of algorithms to aid in the development of improved worst-case algorithms that are useful for large-scale scientific and Internet data analysis problems. In this chapter, I will describe two recent examples---one having to do with selecting good columns or features from a (DNA Single Nucleotide Polymorphism) data matrix, and the other having to do with selecting good clusters or communities from a data graph (representing a social or information network)---that drew on ideas from both areas and that may serve as a model for exploiting complementary algorithmic and statistical perspectives in order to solve applied large-scale data analysis problems.

Connected through hyperedge HE-1 (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-2 (induced by source paper "improved matrix algorithms via the subsampled randomized hadamard transform"):
  with members:
    - "matrix coherence and the nystrom method"
    - "near optimal coresets for least squares regression"
    - "low rank approximation and regression in input sparsity time"
    - "recovering low rank matrices from few coefficients in any basis"
    - "topics in matrix sampling algorithms"
    - "near optimal column based matrix reconstruction"
    - "random projections for k means clustering"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "approximate computation and implicit regularization for very large scale data analysis"):
  with members:
    - "regularized laplacian estimation and fast eigenvector approximation"
    - "the push algorithm for spectral ranking"
    - "randomized algorithms for matrices and data"
    - "empirical comparison of algorithms for network community detection"
    - "implementing regularization implicitly via approximate eigenvector computation"
    - "fast incremental and personalized pagerank"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"
    - "sparse online learning via truncated gradient"

Connected through hyperedge HE-4 (induced by source paper "topics in matrix sampling algorithms"):
  with members:
    - "a matrix hyperbolic cosine algorithm and applications"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"
    - "a nearly mlogn time solver for sdd linear systems"
    - "a unified framework for approximating and clustering data"
    - "lasserre hierarchy higher eigenvalues and approximation schemes for quadratic integer programming with psd objectives"
    - "random projections for k means clustering"
    - "row sampling for matrix algorithms via a non commutative bernstein bound"

Connected through hyperedge HE-5 (induced by source paper "compressed matrix multiplication"):
  with members:
    - "faster algorithms for rectangular matrix multiplication"
    - "deterministic algorithms for skewed matrix products"
    - "better size estimation for sparse matrix products"
    - "the power of simple tabulation hashing"
    - "approximate sparse recovery optimizing time and measurements"
    - "ams without 4 wise independence on product domains"

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)