human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "cp logic a language of causal probabilistic events and its relation to logic programming"):
  Abstract: This papers develops a logical language for representing probabilistic causal laws. Our interest in such a language is twofold. First, it can be motivated as a fundamental study of the representation of causal knowledge. Causality has an inherent dynamic aspect, which has been studied at the semantical level by Shafer in his framework of probability trees. In such a dynamic context, where the evolution of a domain over time is considered, the idea of a causal law as something which guides this evolution is quite natural. In our formalization, a set of probabilistic causal laws can be used to represent a class of probability trees in a concise, flexible and modular way. In this way, our work extends Shafer's by offering a convenient logical representation for his semantical objects. #R##N#Second, this language also has relevance for the area of probabilistic logic programming. In particular, we prove that the formal semantics of a theory in our language can be equivalently defined as a probability distribution over the well-founded models of certain logic programs, rendering it formally quite similar to existing languages such as ICL or PRISM. Because we can motivate and explain our language in a completely self-contained way as a representation of probabilistic causal laws, this provides a new way of explaining the intuitions behind such probabilistic logic programs: we can say precisely which knowledge such a program expresses, in terms that are equally understandable by a non-logician. Moreover, we also obtain an additional piece of knowledge representation methodology for probabilistic logic programs, by showing how they can express probabilistic causal laws.

Includes members:
  Member "using first order probability logic for the construction of bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the complexity of bayesian networks specified by propositional and relational languages")
  Member "stochastic logic programs sampling inference and applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constraint based inference in probabilistic logic programs")
  Member "relational bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review on graph feature learning and feature extraction techniques for link prediction")
  Member "structure based causes and explanations in the independent choice logic":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "causes and explanations in the structural model approach tractable cases")
  Member "clp bn constraint logic programming for probabilistic knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic logic neural networks for reasoning")
  Member "probabilistic reasoning with answer sets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semiring programming a declarative framework for generalized sum product problems")
  Member "a logic for non monotone inductive definitions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "three semantics for modular systems")
  Member "causes and explanations a structural model approach part ii explanations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "interventions and counterfactuals in tractable probabilistic models limitations of contemporary transformations")

Question: Please tell me which class the source paper 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)