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: "causal discovery from a mixture of experimental and observational data"
  Abstract: This paper describes a Bayesian method for combining an arbitrary mixture of observational and experimental data in order to learn causal Bayesian networks. Observational data are passively observed. Experimental data, such as that produced by randomized controlled trials, result from the experimenter manipulating one or more variables (typically randomly) and observing the states of other variables. The paper presents a Bayesian method for learning the causal structure and parameters of the underlying causal process that is generating the data, given that (1) the data contains a mixture of observational and experimental case records, and (2) the causal process is modeled as a causal Bayesian network. This learning method was applied using as input various mixtures of experimental and observational data that were generated from the ALARM causal Bayesian network. In these experiments, the absolute and relative quantities of experimental and observational data were varied systematically. For each of these training datasets, the learning method was applied to predict the causal structure and to estimate the causal parameters that exist among randomly selected pairs of nodes in ALARM that are not confounded. The paper reports how these structure predictions and parameter estimates compare with the true causal structures and parameters as given by the ALARM network.

Connected through hyperedge HE-1 (induced by source paper "counterfactual distribution regression for structured inference"):
  with members:
    - "causal discovery from changes"
    - "kernels on sample sets via nonparametric divergence estimates"

Connected through hyperedge HE-2 (induced by source paper "discovering markov blanket from multiple interventional datasets"):
  with members:
    - "causal decision trees"

Connected through hyperedge HE-3 (induced by source paper "budgeted experiment design for causal structure learning"):
  with members:
    - "counting and uniform sampling from markov equivalent dags"
    - "learning causal structures using regression invariance"
    - "cost optimal learning of causal graphs"
    - "learning causal graphs with small interventions"
    - "causal network inference by optimal causation entropy"
    - "an algorithm for deciding if a set of observed independencies has a causal explanation"
    - "directed information graphs"
    - "bayesian model averaging using the k best bayesian network structures"

Connected through hyperedge HE-4 (induced by source paper "optimal experiment design for causal discovery from fixed number of experiments"):
  with members:
    - "learning causal graphs with small interventions"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"

Connected through hyperedge HE-5 (induced by source paper "joint causal inference from multiple contexts"):
  with members:
    - "domain adaptation by using causal inference to predict invariant conditional distributions"
    - "ancestral causal inference"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "strong completeness and faithfulness in bayesian networks"
    - "learning sparse causal models is not np hard"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "causal discovery from changes"
    - "a general algorithm for deciding transportability of experimental results"

Connected through hyperedge HE-6 (induced by source paper "a bayesian method for causal modeling and discovery under selection"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "asymptotic model selection for directed networks with hidden variables"
    - "evidence absorption and propagation through evidence reversals"
    - "learning gaussian networks"
    - "causal inference in the presence of latent variables and selection bias"

Connected through hyperedge HE-7 (induced by source paper "causal discovery from changes"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-8 (induced by source paper "inequality constraints in causal models with hidden variables"):
  with members:
    - "counterfactual probabilities computational methods bounds and applications"
    - "quantifier elimination for statistical problems"
    - "graphical models and exponential families"
    - "on the testability of causal models with latent and instrumental variables"
    - "instrumentality tests revisited"
    - "a bayesian approach to learning causal networks"
    - "on the testable implications of causal models with hidden variables"
    - "decision theoretic foundations for causal reasoning"

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)