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 "contexts and data quality assessment"):
  Abstract: The quality of data is context dependent. Starting from this intuition and experience, we propose and develop a conceptual framework that captures in formal terms the notion of "context-dependent data quality". We start by proposing a generic and abstract notion of context, and also of its uses, in general and in data management in particular. On this basis, we investigate "data quality assessment" and "quality query answering" as context-dependent activities. A context for the assessment of a database D at hand is modeled as an external database schema, with possibly materialized or virtual data, and connections to external data sources. The database D is put in context via mappings to the contextual schema, which produces a collection C of alternative clean versions of D. The quality of D is measured in terms of its distance to C. The class C} is also used to define and do "quality query answering". The proposed model allows for natural extensions, like the use of data quality predicates, the optimization of the access by the context to external data sources, and also the representation of contexts by means of more expressive ontologies.

Includes members:
  Member "consistency and trust in peer data exchange systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a logic framework for p2p deductive databases")
  Member "composition and inversion of schema mappings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exchanging owl 2 ql knowledge bases")
  Member "query answering in peer to peer data exchange systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a logic framework for p2p deductive databases")

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.DB(Databases)