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: "resolution and datalog rewriting under value invention and equality constraints"
  Abstract: While Datalog is a golden standard for denotational query answering, it does not support value invention or equality constraints. The Datalog ± framework introduced by Gottlob faces these issues by considering rules with fresh variables in the head (known as tgds) or equalities in the head (known as egds). Several tractable classes have been identified, among which: (S) the class of sticky tgds; (T) the class of tgds and egds ensuring oblivious termination; and (G) and the class of guarded tgds. In turn, the tractability of these classes typically relies on the ‘chase’: (S) ensures that every chase derivation is ‘sticky’; (T) ensures polynomial chase termination; and (G) allows stopping the chase after a fixed ‘depth’ while preserving completeness. This paper shows that there are alternative algorithms (instead of the chase) that can serve as a basis for the design of (larger) tractable classes. As a first contribution, we present an algorithm for resolution which is complete for any set of tgds and egds (rather than being complete only for specific subclasses). We then show that a technique of saturation can be used to achieve completeness with respect to First-Order (FO) query rewriting. As an application, we generalize a few existing classes (including (S)) that ensure the existence of a finite FO-rewriting. We then consider a more general notion of rewriting, called Datalog rewriting, and show that it provides a truly unifying paradigm of tractability for the family of Datalog ± languages. While the classes (S), (T) and (G) are incomparable, we show that every set of rules in (S), (T) or (G) can be rewritten into an equivalent set of standard Datalog rules. On the negative side, this means intuitively that Datalog ± does not extend the expressive power of Datalog in the context of query answering. On the positive side however, one may use the flexible syntax of Datalog ± while using (only) standard Datalog in the background, thus making use of existing optimization techniques, such as Magic-Set.

Connected through hyperedge HE-1 (induced by source paper "exchange repairs managing inconsistency in data exchange"):
  with members:
    - "composition with target constraints"
    - "the dlv system for knowledge representation and reasoning"
    - "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)