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: "on the error of random fourier features"
  Abstract: Kernel methods give powerful, flexible, and theoretically grounded approaches to solving many problems in machine learning. The standard approach, however, requires pairwise evaluations of a kernel function, which can lead to scalability issues for very large datasets. Rahimi and Recht (2007) suggested a popular approach to handling this problem, known as random Fourier features. The quality of this approximation, however, is not well understood. We improve the uniform error bound of that paper, as well as giving novel understandings of the embedding's variance, approximation error, and use in some machine learning methods. We also point out that surprisingly, of the two main variants of those features, the more widely used is strictly higher-variance for the Gaussian kernel and has worse bounds.

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 "coke communication censored kernel learning for decentralized non parametric learning"):
  with members:
    - "quantized frank wolfe communication efficient distributed optimization"
    - "online distributed learning over networks in rkh spaces using random fourier features"
    - "sparsified sgd with memory"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "orthogonal random features"
    - "communication efficient learning of deep networks from decentralized data"
    - "a diffusion kernel lms algorithm for nonlinear adaptive networks"
    - "on the equivalence between kernel quadrature rules and random feature expansions"

Connected through hyperedge HE-3 (induced by source paper "differentially private mean embeddings with random features dp merf for simple practical synthetic data generation"):
  with members:
    - "dp cgan differentially private synthetic data and label generation"
    - "differentially private mixed type data generation for unsupervised learning"
    - "differentially private distributed data summarization under covariate shift"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "data synthesis based on generative adversarial networks"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "differentially private generative adversarial network"
    - "differentially private data generative models"

Connected through hyperedge HE-4 (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"
    - "explicit approximations of the gaussian kernel"

Connected through hyperedge HE-5 (induced by source paper "additive function approximation in the brain"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "breaking the curse of dimensionality with convex neural networks"
    - "analysis and extension of arc cosine kernels for large margin classification"

Connected through hyperedge HE-6 (induced by source paper "scalable and interpretable one class svms with deep learning and random fourier features"):
  with members:
    - "efficient gan based anomaly detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "robust deep and inductive anomaly detection"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "not just a black box learning important features through propagating activation differences"

Connected through hyperedge HE-7 (induced by source paper "low precision random fourier features for memory constrained kernel approximation"):
  with members:
    - "high accuracy low precision training"
    - "gaussian quadrature for kernel features"
    - "large scale kernel learning using block coordinate descent"
    - "orthogonal random features"
    - "recursive sampling for the nystr om method"
    - "deep learning with limited numerical precision"
    - "deep fried convnets"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "online distributed learning over networks in rkh spaces using random fourier features"):
  with members:
    - "a diffusion kernel lms algorithm for nonlinear adaptive networks"
    - "efficient klms and krls algorithms a random fourier feature perspective"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "multitask diffusion adaptation over networks"
    - "diffusion adaptation over networks"
    - "extension of wirtinger s calculus to reproducing kernel hilbert spaces and the complex kernel lms"
    - "gossip algorithms for distributed signal processing"

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)