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: "structure based causes and explanations in the independent choice logic"
  Abstract: This paper is directed towards combining Pearl's structural-model approach to causal reasoning with high-level formalisms for reasoning about actions. More precisely, we present a combination of Pearl's structural-model approach with Poole's independent choice logic. We show how probabilistic theories in the independent choice logic can be mapped to probabilistic causal models. This mapping provides the independent choice logic with appealing concepts of causality and explanation from the structural-model approach. We illustrate this along Halpern and Pearl's sophisticated notions of actual cause, explanation, and partial explanation. This mapping also adds first-order modeling capabilities and explicit actions to the structural-model approach.

Connected through hyperedge HE-1 (induced by source paper "causes and explanations in the structural model approach tractable cases"):
  with members:
    - "axiomatizing causal reasoning"
    - "defining explanation in probabilistic systems"
    - "layerwidth analysis of a new metric for directed acyclic graphs"
    - "the dlv system for knowledge representation and reasoning"
    - "what causes a system to satisfy a specification"
    - "responsibility and blame a structural model approach"
    - "causes and explanations a structural model approach part ii explanations"

Connected through hyperedge HE-2 (induced by source paper "cp logic a language of causal probabilistic events and its relation to logic programming"):
  with members:
    - "using first order probability logic for the construction of bayesian networks"
    - "stochastic logic programs sampling inference and applications"
    - "relational bayesian networks"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "probabilistic reasoning with answer sets"
    - "a logic for non monotone inductive definitions"
    - "causes and explanations a structural model approach part ii explanations"
    - "bayesian logic programs"

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)