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: "fastfood approximate kernel expansions in loglinear time"
  Abstract: Despite their successes, what makes kernel methods difficult to use in many large scale problems is the fact that storing and computing the decision function is typically expensive, especially at prediction time. In this paper, we overcome this difficulty by proposing Fastfood, an approximation that accelerates such computation significantly. Key to Fastfood is the observation that Hadamard matrices, when combined with diagonal Gaussian matrices, exhibit properties similar to dense Gaussian random matrices. Yet unlike the latter, Hadamard and diagonal matrices are inexpensive to multiply and store. These two matrices can be used in lieu of Gaussian matrices in Random Kitchen Sinks proposed by Rahimi and Recht (2009) and thereby speeding up the computation for a large range of kernel functions. Specifically, Fastfood requires O(n log d) time and O(n) storage to compute n non-linear basis functions in d dimensions, a significant improvement from O(nd) computation and storage, without sacrificing accuracy. #R##N#Our method applies to any translation invariant and any dot-product kernel, such as the popular RBF kernels and polynomial kernels. We prove that the approximation is unbiased and has low variance. Experiments show that we achieve similar accuracy to full kernel expansions and Random Kitchen Sinks while being 100x faster and using 1000x less memory. These improvements, especially in terms of memory usage, make kernel methods more practical for applications that have large training sets and/or require real-time prediction.

Connected through hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review"):
  with members:
    - "optimized deformed laplacian for spectrum based community detection in sparse heterogeneous graphs"
    - "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "graph reduction by local variation"
    - "scalable spectral clustering using random binning features"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

Connected through hyperedge HE-2 (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"
    - "explicit approximations of the gaussian kernel"

Connected through hyperedge HE-3 (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-4 (induced by source paper "scalable global alignment graph kernel using random features from node embedding to graph embedding"):
  with members:
    - "ddgk learning graph representations for deep divergence graph kernels"
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "scalable spectral clustering using random binning features"
    - "random warping series a random features method for time series embedding"
    - "sparse diffusion convolutional neural networks"
    - "dgcnn disordered graph convolutional neural network based on the gaussian mixture model"
    - "learning convolutional neural networks for graphs"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"

Connected through hyperedge HE-5 (induced by source paper "learning vector valued functions with local rademacher complexity"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "a survey on multi task learning"
    - "orthogonal random features"
    - "a vector contraction inequality for rademacher complexities"
    - "an overview of gradient descent optimization algorithms"
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "a general scoring rule for randomized kernel approximation with application to canonical correlation analysis"):
  with members:
    - "on sampling random features from empirical leverage scores implementation and theoretical guarantees"
    - "but how does it work in theory linear svm with random features"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "data driven random fourier features using stein effect"
    - "stochastic approximation for canonical correlation analysis"
    - "orthogonal random features"
    - "efficient globally convergent stochastic optimization for canonical correlation analysis"
    - "on the equivalence between kernel quadrature rules and random feature expansions"

Connected through hyperedge HE-7 (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"
    - "random feature maps for dot product kernels"
    - "sharp analysis of low rank kernel matrix approximations"

Connected through hyperedge HE-8 (induced by source paper "don t take it lightly phasing optical random projections with unknown operators"):
  with members:
    - "holographic phase retrieval and reference design"
    - "localization from incomplete euclidean distance matrix performance analysis for the svd mds approach"
    - "holographic phase retrieval and optimal reference design"
    - "practical sketching algorithms for low rank matrix approximation"
    - "random projections through multiple optical scattering approximating kernels at the speed of light"
    - "orthogonal random features"
    - "euclidean distance matrices essential theory algorithms and applications"
    - "phase retrieval via wirtinger flow theory and algorithms"

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.LG(Machine Learning)