human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a tutorial on learning with bayesian networks"):
  Abstract: A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention. Three, because the model has both a causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in causal form) and data. Four, Bayesian statistical methods in conjunction with Bayesian networks offer an efficient and principled approach for avoiding the overfitting of data. In this paper, we discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models. With regard to the latter task, we describe methods for learning both the parameters and structure of a Bayesian network, including techniques for learning with incomplete data. In addition, we relate Bayesian-network methods for learning to techniques for supervised and unsupervised learning. We illustrate the graphical-modeling approach using a real-world case study.

Includes members:
  Member "query dags a practical paradigm for implementing belief network inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the complexity of bayesian networks specified by propositional and relational languages")
  Member "approximations in bayesian belief universe for knowledge based systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an advance on variable elimination with applications to tensor based computation")
  Member "learning bayesian networks the combination of knowledge and statistical data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interventional experiment design for causal structure learning")
  Member "asymptotic model selection for directed networks with hidden variables":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on latent tree models and applications")
  Member "directed reduction algorithms and decomposable graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "logarithmic time parallel bayesian inference")
  Member "a transformational characterization of equivalent bayesian network structures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "selective greedy equivalence search finding optimal bayesian networks using a polynomial number of score evaluations")
  Member "computing upper and lower bounds on likelihoods in intractable networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "uncertainty guided continual learning with bayesian neural networks")
  Member "theory refinement on bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "causal datasheet an approximate guide to practically assess bayesian networks in the real world")

Question: Please tell me which class the source paper 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)