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: "towards complex text to sql in cross domain database with intermediate representation"
  Abstract: We present a neural approach called IRNet for complex and cross-domain Text-to-SQL. IRNet aims to address two challenges: 1) the mismatch between intents expressed in natural language (NL) and the implementation details in SQL; 2) the challenge in predicting columns caused by the large number of out-of-domain words. Instead of end-to-end synthesizing a SQL query, IRNet decomposes the synthesis process into three phases. In the first phase, IRNet performs a schema linking over a question and a database schema. Then, IRNet adopts a grammar-based neural model to synthesize a SemQL query which is an intermediate representation that we design to bridge NL and SQL. Finally, IRNet deterministically infers a SQL query from the synthesized SemQL query with domain knowledge. On the challenging Text-to-SQL benchmark Spider, IRNet achieves 46.7% accuracy, obtaining 19.5% absolute improvement over previous state-of-the-art approaches. At the time of writing, IRNet achieves the first position on the Spider leaderboard.

Connected through hyperedge HE-1 (induced by source paper "break it down a question understanding benchmark"):
  with members:
    - "bridging the semantic gap with sql query logs in natural language interfaces to databases"
    - "compositional questions do not necessitate multi hop reasoning"
    - "understanding dataset design choices for multi hop reasoning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "answering complex open domain questions through iterative query generation"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "avoiding reasoning shortcuts adversarial evaluation training and model development for multi hop qa"

Connected through hyperedge HE-2 (induced by source paper "schema2qa answering complex queries on the structured web with a neural model"):
  with members:
    - "genie a generator of natural language semantic parsers for virtual assistant commands"
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "the natural language decathlon multitask learning as question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "typesql knowledge based type aware neural text to sql generation"

Connected through hyperedge HE-3 (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"
    - "sparc cross domain semantic parsing in context"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "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-4 (induced by source paper "bertrand dr improving text to sql using a discriminative re ranker"):
  with members:
    - "grammar based neural text to sql generation"
    - "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-5 (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"
    - "example driven query intent discovery abductive reasoning using semantic similarity"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "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-6 (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"
    - "sparc cross domain semantic parsing in context"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "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-7 (induced by source paper "cosql a conversational text to sql challenge towards cross domain natural language interfaces to databases"):
  with members:
    - "sparc cross domain semantic parsing in context"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "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-8 (induced by source paper "text to sql generation for question answering on electronic medical records"):
  with members:
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "editing based sql query generation for cross domain context dependent questions"
    - "table2answer read the database and answer without sql"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "the natural language decathlon multitask learning as question answering"
    - "bottom up abstractive summarization"
    - "improving text to sql evaluation methodology"

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)