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: "learning bounds for greedy approximation with explicit feature maps from multiple kernels"
  Abstract: Nonlinear kernels can be approximated using finite-dimensional feature maps for efficient risk minimization. Due to the inherent trade-off between the dimension of the (mapped) feature space and the approximation accuracy, the key problem is to identify promising (explicit) features leading to a satisfactory out-of-sample performance. In this work, we tackle this problem by efficiently choosing such features from multiple kernels in a greedy fashion. Our method sequentially selects these explicit features from a set of candidate features using a correlation metric. We establish an out-of-sample error bound capturing the trade-off between the error in terms of explicit features (approximation error) and the error due to spectral properties of the best model in the Hilbert space associated to the combined kernel (spectral error). The result verifies that when the (best) underlying data model is sparse enough, i.e., the spectral error is negligible, one can control the test error with a small number of explicit features, that can scale poly-logarithmically with data. Our empirical results show that given a fixed number of explicit features, the method can achieve a lower test error with a smaller time cost, compared to the state-of-the-art in data-dependent random features.

Connected through hyperedge HE-1 (induced by source paper "generalization guarantees for sparse kernel approximation with entropic optimal features"):
  with members:
    - "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"
    - "explicit approximations of the gaussian kernel"

Connected through hyperedge HE-2 (induced by source paper "multiple kernel learning from u statistics of empirical measures in the feature space"):
  with members:
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (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"
    - "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"
    - "sharp analysis of low rank kernel matrix approximations"

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)