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: "learning bayesian networks the combination of knowledge and statistical data"
  Abstract: We describe algorithms for learning Bayesian networks from a combination of user knowledge and statistical data. The algorithms have two components: a scoring metric and a search procedure. The scoring metric takes a network structure, statistical data, and a user's prior knowledge, and returns a score proportional to the posterior probability of the network structure given the data. The search procedure generates networks for evaluation by the scoring metric. Our contributions are threefold. First, we identify two important properties of metrics, which we call event equivalence and parameter modularity. These properties have been mostly ignored, but when combined, greatly simplify the encoding of a user's prior knowledge. In particular, a user can express her knowledge-for the most part-as a single prior Bayesian network for the domain. Second, we describe local search and annealing algorithms to be used in conjunction with scoring metrics. In the special case where each node has at most one parent, we show that heuristic search can be replaced with a polynomial algorithm to identify the networks with the highest score. Third, we describe a methodology for evaluating Bayesian-network learning algorithms. We apply this approach to a comparison of metrics and search procedures.

Connected through hyperedge HE-1 (induced by source paper "a tutorial on learning with bayesian networks"):
  with members:
    - "query dags a practical paradigm for implementing belief network inference"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "asymptotic model selection for directed networks with hidden variables"
    - "directed reduction algorithms and decomposable graphs"
    - "a transformational characterization of equivalent bayesian network structures"
    - "computing upper and lower bounds on likelihoods in intractable networks"
    - "theory refinement on bayesian networks"
    - "a bayesian approach to learning causal networks"

Connected through hyperedge HE-2 (induced by source paper "interventional experiment design for causal structure learning"):
  with members:
    - "experimental design for cost aware learning of causal graphs"
    - "counting and uniform sampling from markov equivalent dags"
    - "counting and sampling from markov equivalent dags using clique trees"
    - "budgeted experiment design for causal structure learning"
    - "cost optimal learning of causal graphs"
    - "learning causal graphs with small interventions"
    - "lazier than lazy greedy"
    - "an algorithm for deciding if a set of observed independencies has a causal explanation"

Connected through hyperedge HE-3 (induced by source paper "causality based feature selection methods and evaluations"):
  with members:
    - "discovering markov blanket from multiple interventional datasets"
    - "a unified view of causal and non causal feature selection"
    - "a survey of learning causality with data problems and methods"
    - "feature selection a data perspective"
    - "learning identifiable gaussian bayesian networks in polynomial time and sample complexity"
    - "domain adaptation by using causal inference to predict invariant conditional distributions"
    - "forward backward selection with early dropping"
    - "challenges of feature selection for big data analytics"

Connected through hyperedge HE-4 (induced by source paper "dag gnn dag structure learning with graph neural networks"):
  with members:
    - "neural message passing for quantum chemistry"
    - "joint causal inference from multiple contexts"
    - "gated graph sequence neural networks"
    - "adam a method for stochastic optimization"
    - "data analysis with bayesian networks a bootstrap approach"
    - "spectral networks and locally connected networks on graphs"
    - "causal inference in the presence of latent variables and selection bias"
    - "a simple approach for finding the globally optimal bayesian network structure"

Connected through hyperedge HE-5 (induced by source paper "bayesian learning of sum product networks"):
  with members:
    - "spflow an easy and extensible library for deep probabilistic learning using sum product networks"
    - "optimisation of overparametrized sum product networks"
    - "probabilistic deep learning using random sum product networks"
    - "tensorial mixture models"
    - "on the latent variable interpretation in sum product networks"
    - "a unified approach for learning the parameters of sum product networks"
    - "neural architecture search with reinforcement learning"
    - "on the relationship between sum product networks and bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "a meta transfer objective for learning to disentangle causal mechanisms"):
  with members:
    - "on first order meta learning algorithms"
    - "causal reasoning from meta reinforcement learning"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "modular meta learning"
    - "the consciousness prior"
    - "learning independent causal mechanisms"
    - "domain adaptation by using causal inference to predict invariant conditional distributions"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-7 (induced by source paper "bayesian causal inference via probabilistic program synthesis"):
  with members:
    - "soft constraints for inference with declarative knowledge"
    - "bayesian synthesis of probabilistic programs for automatic data modeling"
    - "practical optimal experiment design with probabilistic programs"
    - "being bayesian about network structure"
    - "church a language for generative models"
    - "structured priors for structure learning"

Connected through hyperedge HE-8 (induced by source paper "characterizing distribution equivalence for cyclic and acyclic directed graphs"):
  with members:
    - "a polynomial time algorithm for deciding markov equivalence of directed cyclic graphical models"
    - "a discovery algorithm for directed cyclis graphs"
    - "directed cyclic graphical representations of feedback models"
    - "on the testable implications of causal models with hidden variables"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"
    - "discovering cyclic causal models by independent components analysis"

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.AI(Artificial Intelligence)