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: "constant time predictive distributions for gaussian processes"
  Abstract: One of the most compelling features of Gaussian process (GP) regression is its ability to provide well calibrated posterior distributions. Recent advances in inducing point methods have drastically sped up marginal likelihood and posterior mean computations, leaving posterior covariance estimation and sampling as the remaining computational bottlenecks. In this paper we address this shortcoming by using the Lanczos decomposition algorithm to rapidly approximate the predictive covariance matrix. Our approach, which we refer to as LOVE (LanczOs Variance Estimates), substantially reduces the time and space complexity over any previous method. In practice, it can compute predictive covariances up to 2,000 times faster and draw samples 18,000 time faster than existing methods, all without sacrificing accuracy.

Connected through hyperedge HE-1 (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-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"
    - "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"
    - "a la carte learning fast kernels"
    - "fastfood approximate kernel expansions in loglinear time"

Connected through hyperedge HE-3 (induced by source paper "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"):
  with members:
    - "averaging weights leads to wider optima and better generalization"
    - "product kernel interpolation for scalable gaussian processes"
    - "improved stochastic trace estimation using mutually unbiased bases"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "thoughts on massively scalable gaussian processes"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "deep 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)