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: "thoughts on massively scalable gaussian processes"
  Abstract: We introduce a framework and early results for massively scalable Gaussian processes (MSGP), significantly extending the KISS-GP approach of Wilson and Nickisch (2015). The MSGP framework enables the use of Gaussian processes (GPs) on billions of datapoints, without requiring distributed inference, or severe assumptions. In particular, MSGP reduces the standard $O(n^3)$ complexity of GP learning and inference to $O(n)$, and the standard $O(n^2)$ complexity per test point prediction to $O(1)$. MSGP involves 1) decomposing covariance matrices as Kronecker products of Toeplitz matrices approximated by circulant matrices. This multi-level circulant approximation allows one to unify the orthogonal computational benefits of fast Kronecker and Toeplitz approaches, and is significantly faster than either approach in isolation; 2) local kernel interpolation and inducing points to allow for arbitrarily located data inputs, and $O(1)$ test time predictions; 3) exploiting block-Toeplitz Toeplitz-block structure (BTTB), which enables fast inference and learning when multidimensional Kronecker structure is not present; and 4) projections of the input space to flexibly model correlated inputs and high dimensional data. The ability to handle many ($m \approx n$) inducing points allows for near-exact accuracy and large scale kernel learning.

Connected through hyperedge HE-1 (induced by source paper "recurrent attentive neural process for sequential data"):
  with members:
    - "sequential neural processes"
    - "attentive neural processes"
    - "the functional neural process"
    - "neural processes"
    - "consistent generative query networks"
    - "conditional neural processes"
    - "structural recurrent neural network srnn for group activity analysis"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "scalable gaussian process regression for kernels with a non stationary phase"):
  with members:
    - "exact gaussian processes on a million data points"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "infinite horizon gaussian processes"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "gaussian processes for big data"

Connected through hyperedge HE-3 (induced by source paper "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"):
  with members:
    - "constant time predictive distributions for gaussian processes"
    - "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"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "deep kernel learning"

Connected through hyperedge HE-4 (induced by source paper "constant time predictive distributions for gaussian processes"):
  with members:
    - "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"
    - "gaussian processes for big data"
    - "efficient variational inference in large scale bayesian compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "efficient hyperparameter optimization and infinitely many armed bandits"):
  with members:
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "a stratified analysis of bayesian optimization methods"
    - "least squares revisited scalable approaches for multi class prediction"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "sharp analysis of low rank kernel matrix approximations"
    - "fast cross validation via sequential testing"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-6 (induced by source paper "hyperband a novel bandit based approach to hyperparameter optimization"):
  with members:
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "simple regret for infinitely many armed bandits"
    - "least squares revisited scalable approaches for multi class prediction"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "convolutional neural networks applied to house numbers digit classification"
    - "fast cross validation via sequential testing"
    - "efficient multi start strategies for local search algorithms"

Connected through hyperedge HE-7 (induced by source paper "deep kernel learning"):
  with members:
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "deep fried convnets"
    - "caffe convolutional architecture for fast feature embedding"

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)