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 scalable cur matrix decomposition algorithm lower time complexity and tighter bound"
  Abstract: The CUR matrix decomposition is an important extension of Nystr\"{o}m approximation to a general matrix. It approximates any data matrix in terms of a small number of its columns and rows. In this paper we propose a novel randomized CUR algorithm with an expected relative-error bound. The proposed algorithm has the advantages over the existing relative-error CUR algorithms that it possesses tighter theoretical bound and lower time complexity, and that it can avoid maintaining the whole data matrix in main memory. Finally, experiments on several real-world datasets demonstrate significant improvement over the existing relative-error algorithms.

Connected through hyperedge HE-1 (induced by source paper "seeing the forest from the trees in two looks matrix sketching by cascaded bilateral sampling"):
  with members:
    - "low rank approximation and regression in input sparsity time"
    - "simple and deterministic matrix sketching"
    - "the fast cauchy transform and faster robust linear regression"
    - "fast approximation of matrix coherence and statistical leverage"
    - "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"
    - "improving cur matrix decomposition and the nystr o m approximation via adaptive sampling"
    - "improved bounds for the nystrom method with application to kernel classification"
    - "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"

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)