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: "explicit approximations of the gaussian kernel"
  Abstract: We investigate training and using Gaussian kernel SVMs by approximating the kernel with an explicit finite- dimensional polynomial feature representation based on the Taylor expansion of the exponential. Although not as efficient as the recently-proposed random Fourier features [Rahimi and Recht, 2007] in terms of the number of features, we show how this polynomial representation can provide a better approximation in terms of the computational cost involved. This makes our "Taylor features" especially attractive for use on very large data sets, in conjunction with online or stochastic training.

Connected through hyperedge HE-1 (induced by source paper "generalization guarantees for sparse kernel approximation with entropic optimal features"):
  with members:
    - "learning bounds for greedy approximation with explicit feature maps from multiple kernels"
    - "quadrature based features for kernel approximation"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "a unified optimization view on generalized matching pursuit and frank wolfe"
    - "orthogonal random features"
    - "fastfood approximate kernel expansions in loglinear time"

Connected through hyperedge HE-2 (induced by source paper "oblivious sketching of high degree polynomial kernels"):
  with members:
    - "almost optimal tensor sketch"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "recursive sampling for the nystr om method"
    - "optimal approximate matrix product in terms of stable rank"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-3 (induced by source paper "the gaussiansketch for almost relative error kernel distance"):
  with members:
    - "hashing based estimators for kernel density in high dimensions"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "locality sensitive hashing of curves"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "is input sparsity time possible for kernel low rank approximation"
    - "optimality of the johnson lindenstrauss lemma"
    - "streaming kernel principal component analysis"
    - "fastmmd ensemble of circular discrepancy for efficient two sample test"

Connected through hyperedge HE-4 (induced by source paper "on sampling random features from empirical leverage scores implementation and theoretical guarantees"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "learning bounds for greedy approximation with explicit feature maps from multiple kernels"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "data driven random fourier features using stein effect"
    - "orthogonal random features"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "fastfood approximate kernel expansions in loglinear time"
    - "random feature maps for dot product kernels"

Connected through hyperedge HE-5 (induced by source paper "subspace robust wasserstein distances"):
  with members:
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "generative modeling using the sliced wasserstein distance"
    - "approximating the quadratic transportation metric in near linear time"
    - "advances in pre training distributed word representations"
    - "sliced wasserstein kernels for probability distributions"
    - "learning probability measures with respect to optimal transport metrics"

Connected through hyperedge HE-6 (induced by source paper "no trick treat kernel adaptive filtering using deterministic features"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "online distributed learning over networks in rkh spaces using random fourier features"
    - "gaussian quadrature for kernel features"
    - "on the error of random fourier features"

Connected through hyperedge HE-7 (induced by source paper "approximating the quadratic transportation metric in near linear time"):
  with members:
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "approximate nearest neighbor search in high dimensions"
    - "towards optimal running times for optimal transport"
    - "approximating optimal transport with linear programs"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "optimal transport for domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "exactly robust kernel principal component analysis"):
  with members:
    - "scalable and robust sparse subspace clustering using randomized clustering and multilayer graphs"
    - "robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

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.AI(Artificial Intelligence)