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 "a logic framework for p2p deductive databases"):
  Abstract: This paper presents a logic framework for modeling the interaction among deductive databases in a peer-to-peer (P2P) environment. Each peer joining a P2P system  provides or imports data  from its neighbors by using a set of  mapping rules , that is, a set of semantic correspondences to a set of peers belonging to the same environment. By using mapping rules, as soon as it enters the system, a peer can participate and access all data available in its neighborhood, and through its neighborhood it becomes accessible to all the other peers in the system. A query can be posed to any peer in the system and the answer is computed by using locally stored data and all the information that can be consistently imported from the neighborhood. Two different types of mapping rules are defined: mapping rules allowing to import a maximal set of atoms not leading to inconsistency (called  maximal mapping rules ) and mapping rules allowing to import a minimal set of atoms needed to restore consistency (called  minimal mapping rules ). Implicitly, the use of maximal mapping rules states  it is preferable to import as long as no inconsistencies arise ; whereas the use of minimal mapping rules states that  it is preferable not to import unless a inconsistency exists . The paper presents three different declarative semantics of a P2P system: (i) the  Max Weak Model Semantics , in which mapping rules are used to import  as much knowledge as possible  from a peer’s neighborhood without violating local integrity constraints; (ii) the  Min Weak Model Semantics , in which the P2P system can be locally inconsistent and the information provided by the neighbors is used to restore consistency, that is, to only integrate the missing portion of a correct, but incomplete database; (iii) the  Max-Min Weak Model Semantics  that unifies the previous two different perspectives captured by the Max Weak Model Semantics and Min Weak Model Semantics. This last semantics allows to characterize each peer in the neighborhood as a resource used either to enrich (integrate) or to fix (repair) the knowledge, so as to define a kind of  integrate–repair  strategy for each peer. For each semantics, the paper also introduces an equivalent and alternative characterization, obtained by rewriting each mapping rule into prioritized rules so as to model a P2P system as a prioritized logic program. Finally, results about the computational complexity of P2P logic queries are investigated by considering  brave  and  cautious  reasoning.

Includes members:
  Member "consistency and trust in peer data exchange systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "repair based degrees of database inconsistency computation and complexity")
  Member "distributed reasoning in a peer to peer setting application to the semantic web":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semantic reasoning for context aware internet of things applications")
  Member "query answering in peer to peer data exchange systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "contexts and data quality assessment")
  Member "a framework for compiling preferences in logic programs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "translating lpod and cr prolog2 into standard answer set programs")
  Member "on the problem of computing the well founded semantics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "enhancing global sls resolution with loop cutting and tabling mechanisms")

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.LO(Logic in Computer Science)