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: "scalable gaussian processes with billions of inducing inputs via tensor train decomposition"
  Abstract: We propose a method (TT-GP) for approximate inference in Gaussian Process (GP) models. We build on previous scalable GP research including stochastic variational inference based on inducing inputs, kernel interpolation, and structure exploiting algebra. The key idea of our method is to use Tensor Train decomposition for variational parameters, which allows us to train GPs with billions of inducing inputs and achieve state-of-the-art results on several benchmarks. Further, our approach allows for training kernels based on deep neural networks without any modifications to the underlying GP model. A neural network learns a multidimensional embedding for the data, which is used by the GP to make the final prediction. We train GP and neural network parameters end-to-end without pretraining, through maximization of GP marginal likelihood. We show the efficiency of the proposed approach on several regression and classification benchmark datasets including MNIST, CIFAR-10, and Airline.

Connected through hyperedge HE-1 (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"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "deep kernel learning"
    - "adam a method for stochastic optimization"
    - "a la carte learning fast kernels"
    - "fastfood approximate kernel expansions in loglinear time"

Connected through hyperedge HE-2 (induced by source paper "functional variational bayesian neural networks"):
  with members:
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "differentiable compositional kernel learning for gaussian processes"
    - "eigenvalue corrected noisy natural gradient"
    - "noisy natural gradient as variational inference"
    - "amortised map inference for image super resolution"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "fastfood approximate kernel expansions in loglinear time"
    - "an information theoretic analysis of thompson sampling"

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)