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: "causality in bayesian belief networks"
  Abstract: We address the problem of causal interpretation of the graphical structure of Bayesian belief networks (BBNs). We review the concept of causality explicated in the domain of structural equations models and show that it is applicable to BBNs. In this view, which we call mechanism-based, causality is defined within models and causal asymmetries arise when mechanisms are placed in the context of a system. We lay the link between structural equations models and BBNs models and formulate the conditions under which the latter can be given causal interpretation.

Connected through hyperedge HE-1 (induced by source paper "managing large scale scientific hypotheses as uncertain and probabilistic data"):
  with members:
    - "upsilon db a system for data driven hypothesis management and analytics"
    - "causal networks semantics and expressiveness"
    - "a call to arms revisiting database design"
    - "the complexity of causality and responsibility for query answers and non answers"
    - "conditioning probabilistic databases"
    - "fast and simple relational processing of uncertain data"

Connected through hyperedge HE-2 (induced by source paper "axiomatizing causal reasoning"):
  with members:
    - "defining explanation in probabilistic systems"
    - "decision theoretic foundations for causal reasoning"

Connected through hyperedge HE-3 (induced by source paper "learning bayesian networks the combination of knowledge and statistical data"):
  with members:
    - "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"
    - "learning gaussian networks"

Connected through hyperedge HE-4 (induced by source paper "causal mechanism based model construction"):
  with members:
    - "network fragments representing knowledge for constructing probabilistic models"
    - "a bayesian method for constructing bayesian belief networks from databases"
    - "a generalization of the noisy or model"
    - "object oriented bayesian networks"
    - "spook a system for probabilistic object oriented knowledge representation"

Connected through hyperedge HE-5 (induced by source paper "defining explanation in probabilistic systems"):
  with members:
    - "counterfactual probabilities computational methods bounds and applications"
    - "relevant explanations allowing disjunctive assignments"
    - "generating bayesian networks from probability logic knowledge bases"
    - "from statistical knowledge bases to degrees of belief"
    - "decision theoretic foundations for causal reasoning"

Connected through hyperedge HE-6 (induced by source paper "action networks a framework for reasoning about actions and change under uncertainty"):
  with members:
    - "a decision based view of causality"
    - "argument calculus and networks"
    - "reasoning with qualitative probabilities can be tractable"
    - "from conditional oughts to qualitative decision theory"

Connected through hyperedge HE-7 (induced by source paper "a probabilistic calculus of actions"):
  with members:
    - "counterfactual probabilities computational methods bounds and applications"
    - "argument calculus and networks"
    - "from conditional oughts to qualitative decision theory"

Connected through hyperedge HE-8 (induced by source paper "instrumentality tests revisited"):
  with members:
    - "graphical models and exponential families"
    - "on the testability of causal models with latent and instrumental 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)