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: "improving cur matrix decomposition and the nystr o m approximation via adaptive sampling"
  Abstract: The CUR matrix decomposition and the Nystr\"{o}m approximation are two important low-rank matrix approximation techniques. The Nystr\"{o}m method approximates a symmetric positive semidefinite matrix in terms of a small number of its columns, while CUR approximates an arbitrary data matrix by a small number of its columns and rows. Thus, CUR decomposition can be regarded as an extension of the Nystr\"{o}m approximation. #R##N#In this paper we establish a more general error bound for the adaptive column/row sampling algorithm, based on which we propose more accurate CUR and Nystr\"{o}m algorithms with expected relative-error bounds. The proposed CUR and Nystr\"{o}m algorithms also have low time complexity and can avoid maintaining the whole data matrix in RAM. In addition, we give theoretical analysis for the lower error bounds of the standard Nystr\"{o}m method and the ensemble Nystr\"{o}m method. The main theoretical results established in this paper are novel, and our analysis makes no special assumption on the data matrices.

Connected through hyperedge HE-1 (induced by source paper "parallel mmf a multiresolution approach to matrix computation"):
  with members:
    - "revisiting the nystrom method for improved large scale machine learning"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-2 (induced by source paper "cur algorithm for partially observed matrices"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "optimal cur matrix decompositions"
    - "improved bounds for the nystrom method with application to kernel classification"
    - "a scalable cur matrix decomposition algorithm lower time complexity and tighter bound"
    - "recovering low rank matrices from few coefficients in any basis"
    - "near optimal column based matrix reconstruction"
    - "high rank matrix completion and subspace clustering with missing data"
    - "cur from a sparse optimization viewpoint"

Connected through hyperedge HE-3 (induced by source paper "metric and non metric proximity transformations at linear costs"):
  with members:
    - "efficient algorithms and error analysis for the modified nystrom method"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "supervised learning with similarity functions"
    - "similarity based learning via data driven embeddings"

Connected through hyperedge HE-4 (induced by source paper "revisiting the nystrom method for improved large scale machine learning"):
  with members:
    - "matrix coherence and the nystrom method"
    - "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"
    - "lsrn a parallel iterative solver for strongly over or under determined systems"

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.LG(Machine Learning)