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: "the case for bayesian deep learning"
  Abstract: The key distinguishing property of a Bayesian approach is marginalization instead of optimization, not the prior, or Bayes rule. Bayesian inference is especially compelling for deep neural networks. (1) Neural networks are typically underspecified by the data, and can represent many different but high performing models corresponding to different settings of parameters, which is exactly when marginalization will make the biggest difference for both calibration and accuracy. (2) Deep ensembles have been mistaken as competing approaches to Bayesian methods, but can be seen as approximate Bayesian marginalization. (3) The structure of neural networks gives rise to a structured prior in function space, which reflects the inductive biases of neural networks that help them generalize. (4) The observed correlation between parameters in flat regions of the loss and a diversity of solutions that provide good generalization is further conducive to Bayesian marginalization, as flat regions occupy a large volume in a high dimensional space, and each different solution will make a good contribution to a Bayesian model average. (5) Recent practical advances for Bayesian deep learning provide improvements in accuracy and calibration compared to standard training, while retaining scalability.

Connected through hyperedge HE-1 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization"):
  with members:
    - "fantastic generalization measures and where to find them"
    - "output constrained bayesian neural networks"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "large scale structure of neural network loss landscapes"
    - "functional variational bayesian neural networks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"
    - "understanding generalization through visualizations"

Connected through hyperedge HE-2 (induced by source paper "parameter space factorization for zero shot learning across tasks and languages"):
  with members:
    - "learning and evaluating general linguistic intelligence"
    - "what do language representations really represent"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "mlqa evaluating cross lingual extractive question answering"
    - "how multilingual is multilingual bert"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "cross lingual language model pretraining"
    - "on the cross lingual transferability of monolingual representations"

Connected through hyperedge HE-3 (induced by source paper "learning under model misspecification applications to variational and ensemble methods"):
  with members:
    - "advances in variational inference"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "improving sampling accuracy of stochastic gradient mcmc methods via non uniform subsampling of gradients"):
  with members:
    - "importance sampling for minibatches"
    - "bayesian posterior sampling via stochastic gradient fisher scoring"

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)