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 equivalence between kernel quadrature rules and random feature expansions"
  Abstract: We show that kernel-based quadrature rules for computing integrals can be seen as a special case of random feature expansions for positive definite kernels, for a particular decomposition that always exists for such kernels. We provide a theoretical analysis of the number of required samples for a given approximation error, leading to both upper and lower bounds that are based solely on the eigenvalues of the associated integral operator and match up to logarithmic terms. In particular, we show that the upper bound may be obtained from independent and identically distributed samples from a specific non-uniform distribution, while the lower bound if valid for any set of points. Applying our results to kernel-based quadrature, while our results are fairly general, we recover known upper and lower bounds for the special cases of Sobolev spaces. Moreover, our results extend to the more general problem of full function approximations (beyond simply computing an integral), with results in L2- and L$\infty$-norm that match known results for special cases. Applying our results to random features, we show an improvement of the number of random features needed to preserve the generalization guarantees for learning with Lipschitz-continuous losses.

Connected through hyperedge HE-1 (induced by source paper "convolutional spectral kernel learning"):
  with members:
    - "automated spectral kernel learning"
    - "orthogonal deep neural networks"
    - "functional variational bayesian neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "differentiable compositional kernel learning for gaussian processes"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-2 (induced by source paper "on computation and generalization of generative adversarial imitation learning"):
  with members:
    - "on the global convergence of imitation learning a case for linear quadratic regulator"
    - "learning temporal point processes via reinforcement learning"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "imitating driver behavior with generative adversarial networks"
    - "mmd gan towards deeper understanding of moment matching network"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-3 (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 error of random fourier features"

Connected through hyperedge HE-4 (induced by source paper "reliable estimation of kullback leibler divergence by controlling discriminator complexity in the reproducing kernel hilbert space"):
  with members:
    - "understanding the limitations of variational mutual information estimators"
    - "improving generalization and stability of generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "on convergence and stability of gans"
    - "which training methods for gans do actually converge"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "amortised map inference for image super resolution"
    - "breaking the curse of dimensionality with convex neural networks"

Connected through hyperedge HE-5 (induced by source paper "convergence of adversarial training in overparametrized neural networks"):
  with members:
    - "a gram gauss newton method learning overparameterized deep neural networks for regression problems"
    - "adversarial robustness may be at odds with simplicity"
    - "certified adversarial robustness via randomized smoothing"
    - "intriguing properties of adversarial training"
    - "adversarial training for free"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"

Connected through hyperedge HE-6 (induced by source paper "compression based bound for non compressed network unified generalization error analysis of large compressible deep neural network"):
  with members:
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "stronger generalization bounds for deep nets via a compression approach"

Connected through hyperedge HE-7 (induced by source paper "neural tangent kernels transportation mappings and universal approximation"):
  with members:
    - "a function space view of bounded norm infinite width relu nets the multivariate case"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "what can resnet learn efficiently going beyond kernels"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-8 (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"

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)