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: "askit approximate skeletonization kernel independent treecode in high dimensions"
  Abstract: We present a fast algorithm for kernel summation problems in high-dimensions. These problems appear in computational physics, numerical approximation, non-parametric statistics, and machine learning. In our context, the sums depend on a kernel function that is a pair potential defined on a dataset of points in a high-dimensional Euclidean space. A direct evaluation of the sum scales quadratically with the number of points. Fast kernel summation methods can reduce this cost to linear complexity, but the constants involved do not scale well with the dimensionality of the dataset. #R##N#The main algorithmic components of fast kernel summation algorithms are the separation of the kernel sum between near and far field (which is the basis for pruning) and the efficient and accurate approximation of the far field. #R##N#We introduce novel methods for pruning and approximating the far field. Our far field approximation requires only kernel evaluations and does not use analytic expansions. Pruning is not done using bounding boxes but rather combinatorially using a sparsified nearest-neighbor graph of the input. The time complexity of our algorithm depends linearly on the ambient dimension. The error in the algorithm depends on the low-rank approximability of the far field, which in turn depends on the kernel function and on the intrinsic dimensionality of the distribution of the points. The error of the far field approximation does not depend on the ambient dimension. #R##N#We present the new algorithm along with experimental results that demonstrate its performance. We report results for Gaussian kernel sums for 100 million points in 64 dimensions, for one million points in 1000 dimensions, and for problems in which the Gaussian kernel has a variable bandwidth. To the best of our knowledge, all of these experiments are impossible or prohibitively expensive with existing fast kernel summation methods.

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"
    - "geometry oblivious fmm for compressing dense spd matrices"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "hashing based estimators for kernel density in high dimensions"):
  with members:
    - "coresets for kernel regression"
    - "provably useful kernel matrix approximation in linear time"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "recursive sampling for the nystr om method"
    - "sample optimal density estimation in nearly linear time"

Connected through hyperedge HE-3 (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"
    - "geometry oblivious fmm for compressing dense spd matrices"
    - "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"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-4 (induced by source paper "a study of clustering techniques and hierarchical matrix formats for kernel ridge regression"):
  with members:
    - "on the numerical rank of radial basis function kernels in high dimensions"
    - "a distributed memory package for dense hierarchically semi separable matrix computations using randomization"
    - "far field compression for fast kernel summation methods in high dimensions"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "sharp analysis of low rank kernel matrix approximations"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-5 (induced by source paper "an n log n parallel fast direct solver for kernel matrices"):
  with members:
    - "a distributed memory package for dense hierarchically semi separable matrix computations using randomization"
    - "scalable kernel methods via doubly stochastic gradients"
    - "how to scale up kernel methods to be as good as deep neural nets"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-6 (induced by source paper "fast multipole method as a matrix free hierarchical low rank approximation"):
  with members:
    - "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"
    - "petascale turbulence simulation using a highly parallel fast multipole method on gpus"
    - "fast multipole preconditioners for sparse matrices arising from elliptic equations"
    - "biomolecular electrostatics using a fast multipole bem on up to 512 gpus and a billion unknowns"

Connected through hyperedge HE-7 (induced by source paper "hierarchically compositional kernels for scalable nonparametric learning"):
  with members:
    - "an n log n parallel fast direct solver for kernel matrices"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "hilbert space embeddings of predictive state representations"
    - "kernel based conditional independence test and application in causal discovery"

Connected through hyperedge HE-8 (induced by source paper "inv askit a parallel fast diret solver for kernel matrices"):
  with members:
    - "revisiting the nystrom method for improved large scale machine learning"

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)