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 "enabling reasoning with legalruleml"):
  Abstract: In order to automate verification process, regulatory rules written in natural language needs to be translated into a format that machines can understand. However, none of the existing formalisms can fully represent the elements that appear in legal norms. For instance, most of these formalisms do not provide features to capture the behavior of deontic effects, which is an important aspect in automated compliance checking. This paper presents an approach for transforming legal norms represented using LegalRuleML to a variant of Modal Defeasible Logic (and vice versa) such that legal statement represented using LegalRuleML can be transformed into a machine readable format that can be understand and reasoned about depending upon the client's preferences.

Includes members:
  Member "a general account of argumentation with preferences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cognitive argumentation and the suppression task")
  Member "propositional defeasible logic has linear complexity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rethinking defeasible reasoning a scalable approach")
  Member "representation results for defeasible logic":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rethinking defeasible reasoning a scalable approach")
  Member "a flexible framework for defeasible logics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rethinking defeasible reasoning a scalable approach")

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)