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: "representing schema structure with graph neural networks for text to sql parsing"
  Abstract: Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time. In Spider, a recently-released text-to-SQL dataset, new and complex DBs are given at test time, and so the structure of the DB schema can inform the predicted SQL query. In this paper, we present an encoder-decoder semantic parser, where the structure of the DB schema is encoded with a graph neural network, and this representation is later used at both encoding and decoding time. Evaluation shows that encoding the schema structure improves our parser accuracy from 33.8% to 39.4%, dramatically above the current state of the art, which is at 19.7%.

Connected through hyperedge HE-1 (induced by source paper "how far are we from effective context modeling an exploratory study on semantic parsing in context"):
  with members:
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "cosql a conversational text to sql challenge towards cross domain natural language interfaces to databases"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "sparc cross domain semantic parsing in context"
    - "learning to map context dependent sentences to executable formal queries"
    - "mapping language to code in programmatic context"
    - "allennlp a deep semantic natural language processing platform"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (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"
    - "grammar based neural text to sql generation"
    - "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-3 (induced by source paper "duoquest a dual specification system for expressive sql queries"):
  with members:
    - "bridging the semantic gap with sql query logs in natural language interfaces to databases"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "example driven query intent discovery abductive reasoning using semantic similarity"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"
    - "syntaxsqlnet syntax tree networks for complex and cross domaintext to sql task"

Connected through hyperedge HE-4 (induced by source paper "editing based sql query generation for cross domain context dependent questions"):
  with members:
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "recursive and clause wise decoding for complex and cross domain text to sql generation"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "sparc cross domain semantic parsing in context"
    - "question generation from sql queries improves neural semantic parsing"
    - "learning to map context dependent sentences to executable formal queries"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "cosql a conversational text to sql challenge towards cross domain natural language interfaces to databases"):
  with members:
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "sparc cross domain semantic parsing in context"
    - "editing based sql query generation for cross domain context dependent questions"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "learning to map context dependent sentences to executable formal queries"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sql to text generation with graph to sequence model"
    - "sqlnet generating structured queries from natural language without reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "model based interactive semantic parsing a unified framework and a text to sql case study"):
  with members:
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "learning to learn semantic parsers from natural language supervision"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "quantifying uncertainties in natural language processing tasks"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sql to text generation with graph to sequence model"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "improving text to sql evaluation methodology"

Connected through hyperedge HE-7 (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"
    - "grammar based neural text to sql generation"
    - "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-8 (induced by source paper "rat sql relation aware schema encoding and linking for text to sql parsers"):
  with members:
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "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"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "self attention with relative position representations"
    - "improving text to sql evaluation methodology"
    - "attention is all you need"

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)