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: "exploring query results"
  Abstract: Users typically interact with a database by asking queries and examining results provided by the database system. We refer to the user examining the results of a query and asking follow-up questions as query result exploration. Our work builds on two decades of database community research on provenance. One of the contributions of this paper is that we identify constraints in the context of query result exploration that have previously been unexplored in provenance research. These constraints are useful in optimizing performance and thus improving the user experience during query result exploration. #R##N#Further, three approaches for computing provenance have been described in the literature: (a) in the lazy approach, no additional data is materialized and provenance is computed from the base tables as needed; (b) in the eager approach, additional data is materialized and provenance is computed from this additional materialized data; (c) in the hybrid approach, some additional data is materialized, and provenance is computed using this additional materialized data as well as the base tables. In this paper, we investigate lazy and eager approaches that utilize constraints in the context of query result exploration, as well as novel hybrid approaches, where the keys for some of the base tables are materialized as selected by a cost model. For the TPC-H benchmark, we find that the constraints that we identify are applicable to 19 out of the 22 queries, and result in a better performance of the lazy approach for 7 out of these 19 queries. With materialization, our hybrid approach resulted in a better performance for all but one single table TPC-H query with no joins (where existing approach performed as good as our approach). Further, the performance benefit from our approaches are significant, sometimes several orders of magnitude, compared to previous research.

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.DB(Databases)