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: "robust text to sql generation with execution guided decoding"
  Abstract: We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries. We introduce a new mechanism, execution guidance, to leverage the semantics of SQL. It detects and excludes faulty programs during the decoding procedure by conditioning on the execution of partially generated program. The mechanism can be used with any autoregressive generative model, which we demonstrate on four state-of-the-art recurrent or template-based semantic parsing models. We demonstrate that execution guidance universally improves model performance on various text-to-SQL datasets with different scales and query complexity: WikiSQL, ATIS, and GeoQuery. As a result, we achieve new state-of-the-art execution accuracy of 83.8% on WikiSQL.

Connected through hyperedge HE-1 (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"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "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"

Connected through hyperedge HE-2 (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"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "towards complex text to sql in cross domain database with intermediate representation"):
  with members:
    - "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"
    - "end to end neural entity linking"
    - "improving text to sql evaluation methodology"
    - "semantic parsing with syntax and table aware sql generation"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "neurally guided structure inference"):
  with members:
    - "learning to infer and execute 3d shape programs"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "tree to tree neural networks for program translation"
    - "neural guided constraint logic programming for program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "making neural programming architectures generalize via recursion"
    - "neural sketch learning for conditional program generation"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "when deep learning met code search"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "aroma code recommendation via structural code search"
    - "code2vec learning distributed representations of code"
    - "interpretable lstms for whole brain neuroimaging analyses"
    - "analyzing neuroimaging data through recurrent deep learning models"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "a deep tree based model for software defect prediction"
    - "semantically enhanced software traceability using deep learning techniques"

Connected through hyperedge HE-6 (induced by source paper "zero shot text to sql learning with auxiliary task"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "natural language to structured query generation via meta learning"
    - "incsql training incremental text to sql parsers with non deterministic oracles"
    - "typesql knowledge based type aware neural text to sql generation"
    - "coarse to fine decoding for neural semantic parsing"

Connected through hyperedge HE-7 (induced by source paper "using database rule for weak supervised text to sql generation"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "typesql knowledge based type aware neural text to sql generation"
    - "coarse to fine decoding for neural semantic parsing"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "content enhanced bert based text to sql generation"):
  with members:
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "typesql knowledge based type aware neural text to sql generation"

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)