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 gaussian networks"
  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. Previous work has concentrated on metrics for domains containing only discrete variables, under the assumption that data represents a multinomial sample. In this paper, we extend this work, developing scoring metrics for domains containing all continuous variables or a mixture of discrete and continuous variables, under the assumption that continuous data is sampled from a multivariate normal distribution. Our work extends traditional statistical approaches for identifying vanishing regression coefficients in that we identify two important assumptions, called event equivalence and parameter modularity, that when combined allow the construction of prior distributions for multivariate normal parameters from a single prior Bayesian network specified by a user.

Connected through hyperedge HE-1 (induced by source paper "causal discovery with reinforcement learning"):
  with members:
    - "gradient based neural dag learning"
    - "dag gnn dag structure learning with graph neural networks"
    - "attention is all you need"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "neural architecture search with reinforcement learning"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "experimental design for cost aware learning of causal graphs"):
  with members:
    - "budgeted experiment design for causal structure learning"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"

Connected through hyperedge HE-3 (induced by source paper "combining linear non gaussian acyclic model with logistic regression model for estimating causal structure from mixed continuous and discrete data"):
  with members:
    - "learning discrete bayesian networks from continuous data"

Connected through hyperedge HE-4 (induced by source paper "on learning the structure of bayesian networks and submodular function maximization"):
  with members:
    - "data analysis with bayesian networks a bootstrap approach"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "learning discriminative bayesian networks from high dimensional continuous neuroimaging data"):
  with members:
    - "a construction of bayesian networks from databases based on an mdl principle"
    - "maximum margin bayesian networks"
    - "exact maximum margin structure learning of bayesian networks"
    - "searching for bayesian network structures in the space of restricted acyclic partially directed graphs"

Connected through hyperedge HE-6 (induced by source paper "towards gaussian bayesian network fusion"):
  with members:
    - "aggregating learned probabilistic beliefs"
    - "the topological fusion of bayes nets"
    - "on local optima in learning bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "learning bayesian networks the combination of knowledge and statistical data"):
  with members:
    - "causality in bayesian belief networks"
    - "a definition and graphical representation for causality"
    - "an algorithm for the construction of bayesian network structures from data"
    - "a transformational characterization of equivalent bayesian network structures"
    - "a construction of bayesian networks from databases based on an mdl principle"
    - "using causal information and local measures to learn bayesian networks"
    - "local expression languages for probabilistic dependence a preliminary report"
    - "deriving a minimal i map of a belief network relative to a target ordering of its nodes"

Connected through hyperedge HE-8 (induced by source paper "learning the structure of dynamic probabilistic networks"):
  with members:
    - "structure and parameter learning for causal independence and causal interaction models"
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning bayesian networks with local structure"
    - "tractable inference for complex stochastic processes"
    - "the bayesian structural em algorithm"
    - "theory refinement on bayesian networks"

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)