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: "principles of progress indicators for database repairing"
  Abstract: How should a cleaning system measure the amount of inconsistency in the database? Proper measures are important for quantifying the progress made in the cleaning process relative to the remaining effort and resources required. Similarly to effective progress bars in interactive systems, inconsistency should ideally diminish steadily and continuously while aiming to avoid jitters and jumps. Moreover, measures should be computationally tractable towards online applicability. Building on past research on inconsistency measures for knowledge bases, we embark on a systematic investigation of the rationality postulates of inconsistency measures in the context of database repairing systems. Such postulates should take into consideration the interplay between the database tuples, the integrity constraints, and the space of atomic repairing operations. We shed light on classic measures by examining them against a set of rationality postulates, and propose a measure that is both rationale and tractable for the general class of denial constraints.

Connected through hyperedge HE-1 (induced by source paper "computing optimal repairs for functional dependencies"):
  with members:
    - "repair based degrees of database inconsistency computation and complexity"
    - "measuring and computing database inconsistency via repairs"
    - "holoclean holistic data repairs with probabilistic inference"
    - "prioritized repairing and consistent query answering in relational databases"
    - "complexity of consistent query answering in databases under cardinality based and incremental repair semantics"
    - "minimal change integrity maintenance using tuple deletions"

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)