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 quadrature rules and random features"
  Abstract: We show that kernel-based quadrature rules for computing integrals are 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 known nonuniform 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 L1-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 Lipshitz-continuous losses.

Connected through hyperedge HE-1 (induced by source paper "nearly optimal risk bounds for kernel k means"):
  with members:
    - "scalable kernel k means clustering with nystrom approximation relative error bounds"
    - "learning manifolds with k means and k flats"

Connected through hyperedge HE-2 (induced by source paper "large scale kernel methods and applications to lifelong robot learning"):
  with members:
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "train faster generalize better stability of stochastic gradient descent"
    - "multimodal deep learning for robust rgb d object recognition"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "efficient algorithms and error analysis for the modified nystrom method"
    - "scalable kernel methods via doubly stochastic gradients"

Connected through hyperedge HE-3 (induced by source paper "kernel exponential family estimation via doubly dual embedding"):
  with members:
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "mmd gan towards deeper understanding of moment matching network"
    - "learning from conditional distributions via dual embeddings"
    - "density estimation using real nvp"
    - "scalable kernel methods via doubly stochastic gradients"
    - "probabilistic structured predictors"

Connected through hyperedge HE-4 (induced by source paper "random features for compositional kernels"):
  with members:
    - "sgd learns the conjugate kernel class of the network"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "convolutional kernel networks"
    - "breaking the curse of dimensionality with convex neural networks"
    - "random feature maps for dot product kernels"

Connected through hyperedge HE-5 (induced by source paper "random fourier features for operator valued kernels"):
  with members:
    - "on the error of random fourier features"
    - "a la carte learning fast kernels"

Connected through hyperedge HE-6 (induced by source paper "learning with group invariant features a kernel perspective"):
  with members:
    - "convolutional kernel networks"
    - "unsupervised learning of invariant representations in hierarchical architectures"
    - "gurls a least squares library for supervised learning"
    - "invariant scattering convolution networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-7 (induced by source paper "random maxout features"):
  with members:
    - "gurls a least squares library for supervised learning"

Connected through hyperedge HE-8 (induced by source paper "breaking the curse of dimensionality with convex neural networks"):
  with members:
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "on the computational efficiency of training neural networks"
    - "computational aspects of the hausdorff distance in unbounded dimension"
    - "convex relaxations of structured matrix factorizations"
    - "duality between subgradient and conditional gradient methods"
    - "learning from an optimization viewpoint"
    - "exploring large feature spaces with hierarchical multiple kernel learning"
    - "consistency of the group lasso and multiple kernel learning"

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)