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: "grammar based neural text to sql generation"
  Abstract: The sequence-to-sequence paradigm employed by neural text-to-SQL models typically performs token-level decoding and does not consider generating SQL hierarchically from a grammar. Grammar-based decoding has shown significant improvements for other semantic parsing tasks, but SQL and other general programming languages have complexities not present in logical formalisms that make writing hierarchical grammars difficult. We introduce techniques to handle these complexities, showing how to construct a schema-dependent grammar with minimal over-generation. We analyze these techniques on ATIS and Spider, two challenging text-to-SQL datasets, demonstrating that they yield 14--18\% relative reductions in error.

Connected through hyperedge HE-1 (induced by source paper "bertrand dr improving text to sql using a discriminative re ranker"):
  with members:
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "x sql reinforce schema representation with context"
    - "editing based sql query generation for cross domain context dependent questions"
    - "global reasoning over database structures for text to sql parsing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-2 (induced by source paper "a pilot study for chinese sql semantic parsing"):
  with members:
    - "improving semantic parsing for task oriented dialog"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "is word segmentation necessary for deep learning of chinese representations"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "chinese ner using lattice lstm"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "improving text to sql evaluation methodology"

Connected through hyperedge HE-3 (induced by source paper "representing schema structure with graph neural networks for text to sql parsing"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "improving text to sql evaluation methodology"
    - "allennlp a deep semantic natural language processing platform"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"
    - "learning a neural semantic parser from user feedback"
    - "abstract syntax networks for code generation and semantic parsing"

Connected through hyperedge HE-4 (induced by source paper "global reasoning over database structures for text to sql parsing"):
  with members:
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"
    - "improving neural parsing by disentangling model combination and reranking effects"
    - "abstract syntax networks for code generation and semantic parsing"

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.CL(Computation and Language)