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 "example driven query intent discovery abductive reasoning using semantic similarity"):
  Abstract: Traditional relational data interfaces require precise structured queries over potentially complex schemas. These rigid data retrieval mechanisms pose hurdles for non-expert users, who typically lack language expertise and are unfamiliar with the details of the schema. Query by Example (QBE) methods offer an alternative mechanism: users provide examples of their intended query output and the QBE system needs to infer the intended query. However, these approaches focus on the structural similarity of the examples and ignore the richer context present in the data. As a result, they typically produce queries that are too general, and fail to capture the user's intent effectively. In this paper, we present SQuID, a system that performs semantic similarity-aware query intent discovery. Our work makes the following contributions: (1) We design an end-to-end system that automatically formulates select-project-join queries in an open-world setting, with optional group-by aggregation and intersection operators; a much larger class than prior QBE techniques. (2) We express the problem of query intent discovery using a probabilistic abduction model, that infers a query as the most likely explanation of the provided examples. (3) We introduce the notion of an abduction-ready database, which precomputes semantic properties and related statistics, allowing SQuID to achieve real-time performance. (4) We present an extensive empirical evaluation on three real-world datasets, including user-intent case studies, demonstrating that SQuID is efficient and effective, and outperforms machine learning methods, as well as the state-of-the-art in the related query reverse engineering problem.

Includes members:
  Member "learning from positive and unlabeled data a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fact check worthiness detection as positive unlabelled learning")
  Member "the complexity of reverse engineering problems for conjunctive queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "model comparison games for horn description logics")
  Member "causes for query answers from databases datalog abduction view updates and integrity constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "data management for causal algorithmic fairness")
  Member "compositional vector space models for knowledge base completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vqa lol visual question answering under the lens of logic")
  Member "querying knowledge graphs by example entity tuples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the ubiquity of large graphs and surprising challenges of graph processing extended survey")
  Member "learning and verifying quantified boolean queries by example":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning definable hypotheses on trees")
  Member "coherent integration of databases by abductive logic programming":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "causes for query answers from databases datalog abduction view updates and integrity constraints")

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)