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: "geometry oblivious fmm for compressing dense spd matrices"
  Abstract: We present GOFMM (geometry-oblivious FMM), a novel method that creates a hierarchical low-rank approximation, "compression," of an arbitrary dense symmetric positive definite (SPD) matrix. For many applications, GOFMM enables an approximate matrix-vector multiplication in $N \log N$ or even $N$ time, where $N$ is the matrix size. Compression requires $N \log N$ storage and work. In general, our scheme belongs to the family of hierarchical matrix approximation methods. In particular, it generalizes the fast multipole method (FMM) to a purely algebraic setting by only requiring the ability to sample matrix entries. Neither geometric information (i.e., point coordinates) nor knowledge of how the matrix entries have been generated is required, thus the term "geometry-oblivious." Also, we introduce a shared-memory parallel scheme for hierarchical matrix computations that reduces synchronization barriers. We present results on the Intel Knights Landing and Haswell architectures, and on the NVIDIA Pascal architecture for a variety of matrices.

Connected through hyperedge HE-1 (induced by source paper "matrox modular approach for improving data locality in hierarchical mat rix app rox imation"):
  with members:
    - "a study of clustering techniques and hierarchical matrix formats for kernel ridge regression"
    - "sympiler transforming sparse matrix codes by decoupling symbolic analysis"
    - "a fast block low rank dense solver with applications to finite element matrices"
    - "a distributed memory package for dense hierarchically semi separable matrix computations using randomization"
    - "far field compression for fast kernel summation methods in high dimensions"
    - "askit approximate skeletonization kernel independent treecode in high dimensions"

Connected through hyperedge HE-2 (induced by source paper "matrox a model based algorithm with an efficient storage format for parallel hss structured matrix approximations"):
  with members:
    - "a study of clustering techniques and hierarchical matrix formats for kernel ridge regression"
    - "a fast block low rank dense solver with applications to finite element matrices"
    - "a distributed memory package for dense hierarchically semi separable matrix computations using randomization"
    - "uniform sampling for matrix approximation"
    - "askit approximate skeletonization kernel independent treecode in high dimensions"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "randomized algorithms for matrices and data"

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.NA(Numerical Analysis)