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: "ontology verbalization using semantic refinement"
  Abstract: We propose a rule-based technique to generate redundancy-free NL descriptions of OWL entities.The existing approaches which address the problem of verbalizing OWL ontologies generate NL text segments which are close to their counterpart OWL statements.Some of these approaches also perform grouping and aggregating of these NL text segments to generate a more fluent and comprehensive form of the content.Restricting our attention to description of individuals and concepts, we find that the approach currently followed in the available tools is that of determining the set of all logical conditions that are satisfied by the given individual/concept name and translate these conditions verbatim into corresponding NL descriptions.Human-understandability of such descriptions is affected by the presence of repetitions and redundancies, as they have high fidelity to their OWL this http URL the literature, no efforts had been taken to remove redundancies and repetitions at the logical-level before generating the NL descriptions of entities and we find this to be the main reason for lack of readability of the generated text.Herein, we propose a technique called semantic-refinement(SR) to generate meaningful and easily-understandable descriptions of individuals and concepts of a given OWLontology.We identify the combinations of OWL/DL constructs that lead to repetitive/redundant descriptions and propose a series of refinement rules to rewrite the conditions that are satisfied by an individual/concept in a meaning-preserving manner.The reduced set of conditions are then employed for generating NL descriptions.Our experiments show that, SR leads to significantly improved descriptions of ontology entities.We also test the effectiveness and usefulness of the the generated descriptions for the purpose of validating the ontologies and find that the proposed technique is indeed helpful in the context.

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)