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: "a la carte learning fast kernels"
  Abstract: Kernel methods have great promise for learning rich statistical representations of large modern datasets. However, compared to neural networks, kernel methods have been perceived as lacking in scalability and flexibility. We introduce a family of fast, flexible, lightly parametrized and general purpose kernel learning methods, derived from Fastfood basis function expansions. We provide mechanisms to learn the properties of groups of spectral frequencies in these expansions, which require only O(mlogd) time and O(m) memory, for m basis functions and d input dimensions. We show that the proposed methods can learn a wide class of kernels, outperforming the alternatives in accuracy, speed, and memory consumption.

Connected through hyperedge HE-1 (induced by source paper "subspace inference for bayesian deep learning"):
  with members:
    - "quality of uncertainty quantification for bayesian neural network inference"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "measuring the intrinsic dimension of objective landscapes"
    - "pyro deep universal probabilistic programming"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "averaging weights leads to wider optima and better generalization"
    - "gradient descent happens in a tiny subspace"
    - "fixing variational bayes deterministic variational inference for bayesian neural networks"

Connected through hyperedge HE-2 (induced by source paper "exact gaussian processes on a million data points"):
  with members:
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "constant time predictive distributions for gaussian processes"
    - "product kernel interpolation for scalable gaussian processes"
    - "scalable gaussian processes with billions of inducing inputs via tensor train decomposition"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "deep kernel learning"
    - "adam a method for stochastic optimization"
    - "fastfood approximate kernel expansions in loglinear time"

Connected through hyperedge HE-3 (induced by source paper "scalable kernel learning via the discriminant information"):
  with members:
    - "mmd gan towards deeper understanding of moment matching network"
    - "adam a method for stochastic optimization"
    - "scalable kernel methods via doubly stochastic gradients"
    - "convolutional kernel networks"
    - "how to scale up kernel methods to be as good as deep neural nets"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "function space distributions over kernels"):
  with members:
    - "on the convergence of adam and beyond"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "the human kernel"

Connected through hyperedge HE-5 (induced by source paper "deep learning with t exponential bayesian kitchen sinks"):
  with members:
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "fastfood approximate kernel expansions in loglinear time"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "revisiting random binning features fast convergence and strong parallelizability"):
  with members:
    - "scalable kernel methods via doubly stochastic gradients"
    - "how to scale up kernel methods to be as good as deep neural nets"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "parallel coordinate descent for l1 regularized loss minimization"

Connected through hyperedge HE-7 (induced by source paper "generalization properties of hyper rkhs and its application to out of sample extensions"):
  with members:
    - "local group invariant representations via orbit embeddings"
    - "deep kernel learning"
    - "convolutional kernel networks"
    - "a divide and conquer solver for kernel support vector machines"

Connected through hyperedge HE-8 (induced by source paper "not so random features"):
  with members:
    - "introduction to online convex optimization"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time"
    - "scalable kernel methods via doubly stochastic gradients"

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)