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: "a definition and graphical representation for causality"
  Abstract: We present a precise definition of cause and effect in terms of a fundamental notion called unresponsiveness. Our definition is based on Savage's (1954) formulation of decision theory and departs from the traditional view of causation in that our causal assertions are made relative to a set of decisions. An important consequence of this departure is that we can reason about cause locally, not requiring a causal explanation for every dependency. Such local reasoning can be beneficial because it may not be necessary to determine whether a particular dependency is causal to make a decision. Also in this paper, we examine the graphical encoding of causal relationships. We show that influence diagrams in canonical form are an accurate and efficient representation of causal relationships. In addition, we establish a correspondence between canonical form and Pearl's causal theory.

Connected through hyperedge HE-1 (induced by source paper "learning bayesian networks the combination of knowledge and statistical data"):
  with members:
    - "causality in bayesian belief networks"
    - "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-2 (induced by source paper "identifying independencies in causal graphs with feedback"):
  with members:
    - "counterfactual probabilities computational methods bounds and applications"
    - "causal networks semantics and expressiveness"
    - "on the testability of causal models with latent and instrumental variables"

Connected through hyperedge HE-3 (induced by source paper "evaluating influence diagrams with decision circuits"):
  with members:
    - "decision making using probabilistic inference methods"
    - "bayes ball the rational pastime for determining irrelevance and requisite information in belief networks and influence diagrams"
    - "from influence diagrams to junction trees"
    - "a differential approach to inference in bayesian networks"
    - "sensitivity analysis in bayesian networks from single to multiple parameters"
    - "on the robustness of most probable explanations"

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)