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: "a widely applicable bayesian information criterion"
  Abstract: A statistical model or a learning machine is called regular if the map taking a parameter to a probability distribution is one-to-one and if its Fisher information matrix is always positive definite. If otherwise, it is called singular. In regular statistical models, the Bayes free energy, which is defined by the minus logarithm of Bayes marginal likelihood, can be asymptotically approximated by the Schwarz Bayes information criterion (BIC), whereas in singular models such approximation does not hold. #R##N#Recently, it was proved that the Bayes free energy of a singular model is asymptotically given by a generalized formula using a birational invariant, the real log canonical threshold (RLCT), instead of half the number of parameters in BIC. Theoretical values of RLCTs in several statistical models are now being discovered based on algebraic geometrical methodology. However, it has been difficult to estimate the Bayes free energy using only training samples, because an RLCT depends on an unknown true distribution. #R##N#In the present paper, we define a widely applicable Bayesian information criterion (WBIC) by the average log likelihood function over the posterior distribution with the inverse temperature $1/\log n$, where $n$ is the number of training samples. We mathematically prove that WBIC has the same asymptotic expansion as the Bayes free energy, even if a statistical model is singular for and unrealizable by a statistical model. Since WBIC can be numerically calculated without any information about a true distribution, it is a generalized version of BIC onto singular statistical models.

Connected through hyperedge HE-1 (induced by source paper "subspace inference for bayesian deep learning"):
  with members:
    - "quality of uncertainty quantification for bayesian neural network inference"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "measuring the intrinsic dimension of objective landscapes"
    - "pyro deep universal probabilistic programming"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "averaging weights leads to wider optima and better generalization"
    - "gradient descent happens in a tiny subspace"
    - "fixing variational bayes deterministic variational inference for bayesian neural networks"

Connected through hyperedge HE-2 (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"
    - "asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory"

Connected through hyperedge HE-3 (induced by source paper "differential description length for hyperparameter selection in machine learning"):
  with members:
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "malware in the future forecasting analyst detection of cyber events"):
  with members:
    - "burstiness of intrusion detection process empirical evidence and a modeling approach"
    - "characterizing honeypot captured cyber attacks statistical framework and case study"
    - "predicting cyber attack rates with extreme values"
    - "spatiotemporal patterns and predictability of cyberattacks"

Connected through hyperedge HE-5 (induced by source paper "rebuilding factorized information criterion asymptotically accurate marginal likelihood"):
  with members:
    - "inferring parameters and structure of latent variable models by variational bayes"
    - "on smoothing and inference for topic models"
    - "factorized asymptotic bayesian hidden markov models"

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)