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: "kernel interpolation for scalable structured gaussian processes kiss gp"
  Abstract: We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce kernel approximations for fast computations through kernel interpolation. The SKI framework clarifies how the quality of an inducing point approach depends on the number of inducing (aka interpolation) points, interpolation strategy, and GP covariance kernel. SKI also provides a mechanism to create new scalable kernel methods, through choosing different kernel interpolation strategies. Using SKI, with local cubic kernel interpolation, we introduce KISS-GP, which is 1) more scalable than inducing point alternatives, 2) naturally enables Kronecker and Toeplitz algebra for substantial additional gains in scalability, without requiring any grid data, and 3) can be used for fast and expressive kernel learning. KISS-GP costs O(n) time and storage for GP inference. We evaluate KISS-GP for kernel matrix approximation, kernel learning, and natural sound modelling.

Connected through hyperedge HE-1 (induced by source paper "sleipnir deterministic and provably accurate feature expansion for gaussian process regression with derivatives"):
  with members:
    - "ares and mars adversarial and mmd minimizing regression for sdes"
    - "odin ode informed regression for parameter and state inference in time continuous dynamical systems"
    - "scaling gaussian process regression with derivatives"
    - "modeling and interpolation of the ambient magnetic field by gaussian processes"

Connected through hyperedge HE-2 (induced by source paper "bayesian task embedding for few shot bayesian optimization"):
  with members:
    - "exact gaussian processes on a million data points"
    - "pyro deep universal probabilistic programming"
    - "tensorflow a system for large scale machine learning"
    - "gaussian processes for big data"

Connected through hyperedge HE-3 (induced by source paper "scalable hyperparameter optimization with lazy gaussian processes"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "product kernel interpolation for scalable gaussian processes"
    - "learning transferable architectures for scalable image recognition"
    - "gaussian processes for big data"

Connected through hyperedge HE-4 (induced by source paper "deep kernel transfer in gaussian processes for few shot learning"):
  with members:
    - "simpleshot revisiting nearest neighbor classification for few shot learning"
    - "adaptive deep kernel learning"
    - "meta learning with implicit gradients"
    - "a closer look at few shot classification"
    - "few shot learning with metric agnostic conditional embeddings"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reconciling meta learning and continual learning with online mixtures of tasks"
    - "learning embedding adaptation for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "botorch programmable bayesian optimization in pytorch"):
  with members:
    - "probo a framework for using probabilistic programming in bayesian optimization"
    - "probo versatile bayesian optimization using any probabilistic programming language"
    - "practical multi fidelity bayesian optimization for hyperparameter tuning"
    - "subspace inference for bayesian deep learning"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "pyro deep universal probabilistic programming"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"

Connected through hyperedge HE-6 (induced by source paper "adaptive deep kernel learning"):
  with members:
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "attentive neural processes"
    - "neural processes"
    - "bayesian model agnostic meta learning"
    - "conditional neural processes"
    - "deep sets"
    - "meta learning with differentiable closed form solvers"

Connected through hyperedge HE-7 (induced by source paper "deep probabilistic kernels for sample efficient learning"):
  with members:
    - "sampled softmax with random fourier features"
    - "a closer look at few shot classification"
    - "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"
    - "infrastructure quality assessment in africa using satellite imagery and deep learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "deep bayesian active learning with image data"

Connected through hyperedge HE-8 (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"
    - "deep kernel learning"
    - "adam a method for stochastic optimization"
    - "a la carte learning fast kernels"
    - "fastfood approximate kernel expansions in loglinear time"

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)