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: "gaussian processes for big data"
  Abstract: We introduce stochastic variational inference for Gaussian process models. This enables the application of Gaussian process (GP) models to data sets containing millions of data points. We show how GPs can be vari- ationally decomposed to depend on a set of globally relevant inducing variables which factorize the model in the necessary manner to perform variational inference. Our ap- proach is readily extended to models with non-Gaussian likelihoods and latent variable models based around Gaussian processes. We demonstrate the approach on a simple toy problem and two real world data sets.

Connected through hyperedge HE-1 (induced by source paper "scalable uncertainty for computer vision with functional variational inference"):
  with members:
    - "deepfactors real time probabilistic dense monocular slam"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "functional variational bayesian neural networks"
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "evaluating bayesian deep learning methods for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "energy based processes for exchangeable data"):
  with members:
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "neural similarity learning"
    - "the functional neural process"
    - "conditional neural processes"
    - "pointgrow autoregressively learned point cloud generation with self attention"
    - "foldingnet point cloud auto encoder via deep grid deformation"
    - "vconv dae deep volumetric shape learning without object labels"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"

Connected through hyperedge HE-3 (induced by source paper "localising faster efficient and precise lidar based robot localisation in large scale environments"):
  with members:
    - "segmap segment based mapping and localization using data driven descriptors"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep auxiliary learning for visual localization and odometry"
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "geometric loss functions for camera pose regression with deep learning"
    - "modelling uncertainty in deep learning for camera relocalization"

Connected through hyperedge HE-4 (induced by source paper "multi source deep gaussian process kernel learning"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "interpretable deep gaussian processes with moments"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "deep multi fidelity gaussian processes"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "deep kernel learning"
    - "generalized denoising auto encoders as generative models"
    - "what regularized auto encoders learn from the data generating distribution"

Connected through hyperedge HE-5 (induced by source paper "gaussian process policy optimization"):
  with members:
    - "evolved policy gradients"
    - "parameter space noise for exploration"
    - "curiosity driven exploration by self supervised prediction"
    - "proximal policy optimization algorithms"
    - "improving policy gradient by exploring under appreciated rewards"
    - "trust region policy optimization"

Connected through hyperedge HE-6 (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"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"

Connected through hyperedge HE-7 (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"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"

Connected through hyperedge HE-8 (induced by source paper "a survey of optimization methods from a machine learning perspective"):
  with members:
    - "stochastic conjugate gradient algorithm with variance reduction"
    - "compatible natural gradient policy search"
    - "on the convergence of adam and beyond"
    - "normalized direction preserving adam"
    - "recent advances in recurrent neural networks"
    - "task agnostic meta learning for few shot learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "prototypical networks for few shot 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)