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: "scalable partial least squares regression on grammar compressed data matrices"
  Abstract: With massive high-dimensional data now commonplace in research and industry, there is a strong and growing demand for more scalable computational techniques for data analysis and knowledge discovery. Key to turning these data into knowledge is the ability to learn statistical models with high interpretability. Current methods for learning statistical models either produce models that are not interpretable or have prohibitive computational costs when applied to massive data. In this paper we address this need by presenting a scalable algorithm for partial least squares regression (PLS), which we call compression-based PLS (cPLS), to learn predictive linear models with a high interpretability from massive high-dimensional data. We propose a novel grammar-compressed representation of data matrices that supports fast row and column access while the data matrix is in a compressed form. The original data matrix is grammar-compressed and then the linear model in PLS is learned on the compressed data matrix, which results in a significant reduction in working space, greatly improving scalability. We experimentally test cPLS on its ability to learn linear models for classification, regression and feature extraction with various massive high-dimensional data, and show that cPLS performs superiorly in terms of prediction accuracy, computational efficiency, and interpretability.

Connected through hyperedge HE-1 (induced by source paper "re pair in place"):
  with members:
    - "a study for image compression using re pair algorithm"
    - "a faster external memory priority queue with decreasekeys"
    - "mr repair grammar compression based on maximal repeats"
    - "repair in compressed space and time"
    - "burrows wheeler transform and lcp array construction in constant space"
    - "practical and effective re pair compression"
    - "optimal time and space construction of suffix arrays and lcp arrays for integer alphabets"
    - "optimal in place suffix sorting"

Connected through hyperedge HE-2 (induced by source paper "practical repetition aware grammar compression"):
  with members:
    - "mr repair grammar compression based on maximal repeats"
    - "repair in compressed space and time"
    - "optimal time text indexing in bwt runs bounded space"
    - "space efficient re pair compression"
    - "online pattern matching for string edit distance with moves"
    - "compact q gram profiling of compressed strings"
    - "a succinct grammar compression"
    - "approximation of grammar based compression via recompression"

Connected through hyperedge HE-3 (induced by source paper "mr repair grammar compression based on maximal repeats"):
  with members:
    - "representing the suffix tree with the cdawg"
    - "linear size cdawg new repetition aware indexing and grammar compression"
    - "fast label extraction in the cdawg"
    - "space efficient re pair compression"
    - "composite repetition aware data structures"

Connected through hyperedge HE-4 (induced by source paper "space efficient re pair compression"):
  with members:
    - "radix sorting with no extra space"

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)