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: "strong completeness and faithfulness in bayesian networks"
  Abstract: A completeness result for d-separation applied to discrete Bayesian networks is presented and it is shown that in a strong measure-theoretic sense almost all discrete distributions for a given network structure are faithful; i.e. the independence facts true of the distribution are all and only those entailed by the network structure.

Connected through hyperedge HE-1 (induced by source paper "understanding agent incentives using causal influence diagrams part i single action settings"):
  with members:
    - "modeling agi safety frameworks with causal influence diagrams"
    - "agi safety literature review"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "ai safety gridworlds"
    - "on formalizing fairness in prediction with machine learning"
    - "a game theoretic analysis of the off switch game"
    - "good and safe uses of ai oracles"

Connected through hyperedge HE-2 (induced by source paper "on the bias of directed information estimators"):
  with members:
    - "k nn estimation of directed information"
    - "estimating the directed information and testing for causality"
    - "universal estimation of directed information"
    - "directed information graphs"

Connected through hyperedge HE-3 (induced by source paper "causal discovery from heterogeneous nonstationary data"):
  with members:
    - "discovery and visualization of nonstationary causal models"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "kernel based conditional independence test and application in causal discovery"

Connected through hyperedge HE-4 (induced by source paper "measuring sample path causal influences with relative entropy"):
  with members:
    - "on the bias of directed information estimators"
    - "a sample path measure of causal influence"
    - "consistent tomography under partial observations over adaptive networks"
    - "k nn estimation of directed information"
    - "estimating the directed information and testing for causality"
    - "jidt an information theoretic toolkit for studying the dynamics of complex systems"
    - "causal networks semantics and expressiveness"
    - "universal estimation of directed information"

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"
    - "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"
    - "causal discovery from a mixture of experimental and observational data"

Connected through hyperedge HE-6 (induced by source paper "learning causal graphs with small interventions"):
  with members:
    - "an algorithm for deciding if a set of observed independencies has a causal explanation"
    - "causal inference and causal explanation with background knowledge"
    - "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"

Connected through hyperedge HE-7 (induced by source paper "causal inference in the presence of latent variables and selection bias"):
  with members:
    - "an algorithm for the construction of bayesian network structures from data"
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-8 (induced by source paper "identifying the relevant nodes without learning the model"):
  with members:
    - "bayes ball the rational pastime for determining irrelevance and requisite information in belief networks and influence diagrams"
    - "constructing situation specific belief networks"
    - "lazy propagation in junction trees"
    - "computational advantages of relevance reasoning in bayesian belief networks"
    - "finding optimal 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)