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 "duoquest a dual specification system for expressive sql queries"):
  Abstract: Querying a relational database is difficult because it requires users to know both the SQL language and be familiar with the schema. On the other hand, many users possess enough domain familiarity or expertise to describe their desired queries by alternative means. For such users, two major alternatives to writing SQL are natural language interfaces (NLIs) and programming-by-example (PBE). Both of these alternatives face certain pitfalls: natural language queries (NLQs) are often ambiguous, even for human interpreters, while current PBE approaches require either low-complexity queries, user schema knowledge, exact example tuples from the user, or a closed-world assumption to be tractable. Consequently, we propose dual-specification query synthesis, which consumes both a NLQ and an optional PBE-like table sketch query that enables users to express varied levels of domain-specific knowledge. We introduce the novel dual-specification Duoquest system, which leverages guided partial query enumeration to efficiently explore the space of possible queries. We present results from user studies in which Duoquest demonstrates a 62.5% absolute increase in query construction accuracy over a state-of-the-art NLI and comparable accuracy to a PBE system on a more limited workload supported by the PBE system. In a simulation study on the prominent Spider benchmark, Duoquest demonstrates a >2x increase in top-1 accuracy over both NLI and PBE.

Includes members:
  Member "bridging the semantic gap with sql query logs in natural language interfaces to databases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "break it down a question understanding benchmark")
  Member "towards complex text to sql in cross domain database with intermediate representation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "break it down a question understanding benchmark")
  Member "example driven query intent discovery abductive reasoning using semantic similarity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "data invariants on trust in data driven systems")
  Member "representing schema structure with graph neural networks for text to sql parsing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "how far are we from effective context modeling an exploratory study on semantic parsing in context")
  Member "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "break it down a question understanding benchmark")
  Member "syntaxsqlnet syntax tree networks for complex and cross domaintext to sql task":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generating logical forms from graph representations of text and entities")

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)