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: "type and content driven synthesis of sql queries from natural language"
  Abstract: This paper presents a new technique for automatically synthesizing SQL queries from natural language. Our technique is fully automated, works for any database without requiring additional customization, and does not require users to know the underlying database schema. Our method achieves these goals by combining natural language processing, program synthesis, and automated program repair. Given the user's English description, our technique first uses semantic parsing to generate a query sketch, which is subsequently completed using type-directed program synthesis and assigned a confidence score using database contents. However, since the user's description may not accurately reflect the actual database schema, our approach also performs fault localization and repairs the erroneous part of the sketch. This synthesize-repair loop is repeated until the algorithm infers a query with a sufficiently high confidence score. We have implemented the proposed technique in a tool called Sqlizer and evaluate it on three different databases. Our experiments show that the desired query is ranked within the top 5 candidates in close to 90% of the cases.

Connected through hyperedge HE-1 (induced by source paper "semantic parsing with syntax and table aware sql generation"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "inferring and executing programs for visual reasoning"
    - "bidirectional attention for sql generation"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "learning to reason end to end module networks for visual question answering"
    - "weakly supervised semantic parsing with abstract examples"
    - "learning a neural semantic parser from user feedback"

Connected through hyperedge HE-2 (induced by source paper "discourse in multimedia a case study in information extraction"):
  with members:
    - "rhetorical relations for information retrieval"
    - "a syntactic neural model for general purpose code generation"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "latent predictor networks for code generation"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"
    - "learning dependency based compositional semantics"
    - "a pdtb styled end to end discourse parser"
    - "pattern matching and discourse processing in information extraction from japanese text"

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)