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: "asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory"
  Abstract: In regular statistical models, the leave-one-out cross-validation is asymptotically equivalent to the Akaike information criterion. However, since many learning machines are singular statistical models, the asymptotic behavior of the cross-validation remains unknown. In previous studies, we established the singular learning theory and proposed a widely applicable information criterion, the expectation value of which is asymptotically equal to the average Bayes generalization loss. In the present paper, we theoretically compare the Bayes cross-validation loss and the widely applicable information criterion and prove two theorems. First, the Bayes cross-validation loss is asymptotically equivalent to the widely applicable information criterion as a random variable. Therefore, model selection and hyperparameter optimization using these two values are asymptotically equivalent. Second, the sum of the Bayes generalization error and the Bayes cross-validation error is asymptotically equal to $2\lambda/n$, where $\lambda$ is the real log canonical threshold and $n$ is the number of training samples. Therefore the relation between the cross-validation error and the generalization error is determined by the algebraic geometrical structure of a learning machine. We also clarify that the deviance information criteria are different from the Bayes cross-validation and the widely applicable information criterion.

Connected through hyperedge HE-1 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "faster autoaugment learning augmentation strategies using backpropagation"
    - "starnet targeted computation for object detection in point clouds"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "your classifier is secretly an energy based model and you should treat it like one"
    - "efficientdet scalable and efficient object detection"

Connected through hyperedge HE-2 (induced by source paper "bayesian variational autoencoders for unsupervised out of distribution detection"):
  with members:
    - "a cold approach to generating optimal samples"
    - "conditioning by adaptive sampling for robust design"
    - "all smiles variational autoencoder"
    - "detecting semantic anomalies"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "icebreaker element wise active information acquisition with bayesian deep latent gaussian model"
    - "efficient gan based anomaly detection"
    - "uncertainty in the variational information bottleneck"

Connected through hyperedge HE-3 (induced by source paper "gpdoemd a python package for design of experiments for model discrimination"):
  with members:
    - "automatic differentiation in machine learning a survey"
    - "input design for model discrimination and fault detection via convex relaxation"
    - "automated derivation of the adjoint of high level transient finite element programs"
    - "a widely applicable bayesian information criterion"

Connected through hyperedge HE-4 (induced by source paper "automated model selection with bayesian quadrature"):
  with members:
    - "improving quadrature for constrained integrands"
    - "on the equivalence between kernel quadrature rules and random feature expansions"

Connected through hyperedge HE-5 (induced by source paper "probabilistically autoencoded horseshoe disentangled multidomain item response theory models"):
  with members:
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "disentangled sequential autoencoder"

Connected through hyperedge HE-6 (induced by source paper "a survey of discrete methods in algebraic statistics for networks"):
  with members:
    - "the switch markov chain for sampling irregular graphs"
    - "ergms are hard"

Connected through hyperedge HE-7 (induced by source paper "bayesian cross validation and waic for predictive prior design in regular asymptotic theory"):
  with members:
    - "equations of states in singular statistical estimation"

Connected through hyperedge HE-8 (induced by source paper "a widely applicable bayesian information criterion"):
  with members:
    - "asymptotic model selection for naive bayesian networks"
    - "asymptotic learning curve and renormalizable condition in statistical learning theory"

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)