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 "building a neural semantic parser from a domain ontology"):
  Abstract: Semantic parsing is the task of converting natural language utterances into machine interpretable meaning representations which can be executed against a real-world environment such as a database. Scaling semantic parsing to arbitrary domains faces two interrelated challenges: obtaining broad coverage training data effectively and cheaply; and developing a model that generalizes to compositional utterances and complex intentions. We address these challenges with a framework which allows to elicit training data from a domain ontology and bootstrap a neural parser which recursively builds derivations of logical forms. In our framework meaning representations are described by sequences of natural language templates, where each template corresponds to a decomposed fragment of the underlying meaning representation. Although artificial, templates can be understood and paraphrased by humans to create natural utterances, resulting in parallel triples of utterances, meaning representations, and their decompositions. These allow us to train a neural semantic parser which learns to compose rules in deriving meaning representations. We crowdsource training data on six domains, covering both single-turn utterances which exhibit rich compositionality, and sequential utterances where a complex task is procedurally performed in steps. We then develop neural semantic parsers which perform such compositional tasks. In general, our approach allows to deploy neural semantic parsers quickly and cheaply from a given domain ontology.

Includes members:
  Member "seq2sql generating structured queries from natural language using reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base")
  Member "sequence to action end to end semantic graph generation for semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semantic parsing with dual learning")
  Member "tranx a transition based neural abstract syntax parser for semantic parsing and code generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structural language models of code")
  Member "semantic parsing for task oriented dialog using hierarchical representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "don t parse generate a sequence to sequence architecture for task oriented semantic parsing")
  Member "universal semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "making neural programming architectures generalize via recursion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "progress extrapolating algorithmic learning to arbitrary sequence lengths")
  Member "neural semantic parsing over multiple knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semantic parsing with dual learning")
  Member "learning structured natural language representations for semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploring neural models for parsing natural language into first order logic")

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.CL(Computation and Language)