human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "matrox modular approach for improving data locality in hierarchical mat rix app rox imation"):
  Abstract: Hierarchical matrix approximations have gained significant traction in the machine learning and scientific community as they exploit available low-rank structures in kernel methods to compress the kernel matrix. The resulting compressed matrix, HMatrix, is used to reduce the computational complexity of operations such as HMatrix-matrix multiplications with tuneable accuracy in an evaluation phase. Existing implementations of HMatrix evaluations do not preserve locality and often lead to unbalanced parallel execution with high synchronization. Also, current solutions require the compression phase to re-execute if the kernel method or the required accuracy change. In this work, we describe MatRox, a framework that uses novel structure analysis strategies, blocking and coarsen, with code specialization and a storage format to improve locality and create load-balanced parallel tasks for HMatrix-matrix multiplications. Modularization of the matrix compression phase enables the reuse of computations when there are changes to the input accuracy and the kernel function. The MatRox-generated code for matrix-matrix multiplication is 2.98x, 1.60x, and 5.98x faster than library implementations available in GOFMM, SMASH, and STRUMPACK respectively. Additionally, the ability to reuse portions of the compression computation for changes to the accuracy leads to up to 2.64x improvement with MatRox over five changes to accuracy using GOFMM.

Includes members:
  Member "a study of clustering techniques and hierarchical matrix formats for kernel ridge regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "matrox a model based algorithm with an efficient storage format for parallel hss structured matrix approximations")
  Member "sympiler transforming sparse matrix codes by decoupling symbolic analysis":
    also connected through other hyperedges:
      (none)
  Member "geometry oblivious fmm for compressing dense spd matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "matrox a model based algorithm with an efficient storage format for parallel hss structured matrix approximations")
  Member "a fast block low rank dense solver with applications to finite element matrices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "geostatistical modeling and prediction using mixed precision tile cholesky factorization")
  Member "a distributed memory package for dense hierarchically semi separable matrix computations using randomization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "matrox a model based algorithm with an efficient storage format for parallel hss structured matrix approximations")
  Member "far field compression for fast kernel summation methods in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a study of clustering techniques and hierarchical matrix formats for kernel ridge regression")
  Member "askit approximate skeletonization kernel independent treecode in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hashing based estimators for kernel density in high dimensions")

Question: Please tell me which class the source paper 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.DC(Distributed, Parallel, and Cluster Computing)