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: "splitting and updating hybrid knowledge bases"
  Abstract: Over the years, nonmonotonic rules have proven to be a very expressive and useful knowledge representation paradigm. They have recently been used to complement the expressive power of Description Logics (DLs), leading to the study of integrative formal frameworks, generally referred to as hybrid knowledge bases, where both DL axioms and rules can be used to represent knowledge. The need to use these hybrid knowledge bases in dynamic domains has called for the development of update operators, which, given the substantially different way DLs and rules are usually updated, has turned out to be an extremely difficult task. In Slota and Leite (2010b Towards Closed World Reasoning in Dynamic Open Worlds. Theory and Practice of Logic Programming, 26th Int’l. Conference on Logic Programming (ICLP’10) Special Issue 10(4–6) (July), 547–564.), a first step towards addressing this problem was taken, and an update operator for hybrid knowledge bases was proposed. Despite its significance— not only for being the first update operator for hybrid knowledge bases in the literature, but also because it has some applications—this operator was defined for a restricted class of problems where only the ABox was allowed to change, which considerably diminished its applicability. Many applications that use hybrid knowledge bases in dynamic scenarios require both DL axioms and rules to be updated. In this paper, motivated by real world applications, we introduce an update operator for a large class of hybrid knowledge bases where both the DL component as well as the rule component are allowed to dynamically change. We introduce splitting sequences and splitting theorem for hybrid knowledge bases, use them to define a modular update semantics, investigate its basic properties, and illustrate its use on a realistic example about cargo imports.

Connected through hyperedge HE-1 (induced by source paper "exception based knowledge updates"):
  with members:
    - "the rise and fall of semantic rule updates based on se models"
    - "on the evolution of the instance level of dl lite knowledge bases"
    - "updates in answer set programming an approach based on basic structural properties"
    - "an abductive framework for computing knowledge base updates"
    - "strong equivalence made easy nested expressions and weight constraints"
    - "on properties of update sequences based on causal rejection"

Connected through hyperedge HE-2 (induced by source paper "towards efficient evolving multi context systems preliminary report"):
  with members:
    - "multi context systems for reactive reasoning in dynamic environments"
    - "the rise and fall of semantic rule updates based on se models"

Connected through hyperedge HE-3 (induced by source paper "the rise and fall of semantic rule updates based on se models"):
  with members:
    - "a program level approach to revising logic programs under the answer set semantics"
    - "propositional theories are strongly equivalent to logic programs"
    - "updates in answer set programming an approach based on basic structural properties"
    - "an abductive framework for computing knowledge base updates"
    - "strong equivalence made easy nested expressions and weight constraints"
    - "on properties of update sequences based on causal rejection"

Connected through hyperedge HE-4 (induced by source paper "query driven procedures for hybrid mknf knowledge bases"):
  with members:
    - "towards closed world reasoning in dynamic open worlds"
    - "conjunctive query answering for the description logic shiq"

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)