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: "seq2sql generating structured queries from natural language using reinforcement learning"
  Abstract: Relational databases store a significant amount of the worlds data. However, accessing this data currently requires users to understand a query language such as SQL. We propose Seq2SQL, a deep neural network for translating natural language questions to corresponding SQL queries. Our model uses rewards from in the loop query execution over the database to learn a policy to generate the query, which contains unordered parts that are less suitable for optimization via cross entropy loss. Moreover, Seq2SQL leverages the structure of SQL to prune the space of generated queries and significantly simplify the generation problem. In addition to the model, we release WikiSQL, a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables fromWikipedia that is an order of magnitude larger than comparable datasets. By applying policy based reinforcement learning with a query execution environment to WikiSQL, Seq2SQL outperforms a state-of-the-art semantic parser, improving execution accuracy from 35.9% to 59.4% and logical form accuracy from 23.4% to 48.3%.

Connected through hyperedge HE-1 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base"):
  with members:
    - "generating logical forms from graph representations of text and entities"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "neural query language a knowledge base query language for tensorflow"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "neural compositional denotational semantics for question answering"
    - "improving text to sql evaluation methodology"
    - "embedding logical queries on knowledge graphs"
    - "open domain question answering using early fusion of knowledge bases and text"

Connected through hyperedge HE-2 (induced by source paper "exploring neural models for parsing natural language into first order logic"):
  with members:
    - "probing neural network comprehension of natural language arguments"
    - "semantic parsing with syntax and table aware sql generation"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"
    - "attention is all you need"
    - "found in translation reconstructing phylogenetic language trees from translations"
    - "learning a neural semantic parser from user feedback"
    - "robust incremental neural semantic graph parsing"
    - "a deep generative framework for paraphrase generation"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "scrutinizer a mixed initiative approach to large scale data driven claim verification"):
  with members:
    - "explainable fact checking with probabilistic answer set programming"
    - "claimrank detecting check worthy claims in arabic and english"
    - "automated fact checking task formulations methods and future directions"
    - "fact checking in community forums"
    - "discriminative predicate path mining for fact checking in knowledge graphs"
    - "the rise of social bots"
    - "computational fact checking from knowledge networks"
    - "believe it or not adding belief annotations to databases"

Connected through hyperedge HE-5 (induced by source paper "flowsense a natural language interface for visual data exploration within a dataflow system"):
  with members:
    - "recent trends in deep learning based natural language processing"
    - "iris a conversational agent for complex tasks"
    - "a brief survey of text mining classification clustering and extraction techniques"
    - "compositional semantic parsing on semi structured tables"

Connected through hyperedge HE-6 (induced by source paper "on the evaluation of intelligent process automation"):
  with members:
    - "video captioning with transferred semantic attributes"
    - "a dataset for movie description"
    - "using descriptive video services to create a large data source for video annotation research"

Connected through hyperedge HE-7 (induced by source paper "why build an assistant in minecraft"):
  with members:
    - "craftassist a framework for dialogue enabled interactive agents"
    - "habitat a platform for embodied ai research"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "towards deep conversational recommendations"
    - "learning to ask questions in open domain conversational systems with typed decoders"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"

Connected through hyperedge HE-8 (induced by source paper "introduction to neural network based approaches for question answering over knowledge graphs"):
  with members:
    - "pretrained transformers for simple question answering over knowledge graphs"
    - "learning to generalize from sparse and underspecified rewards"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "practical semantic parsing for spoken language understanding"
    - "question generation from sql queries improves neural semantic parsing"
    - "towards a question answering system over the semantic web"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "sqlnet generating structured queries from natural language without reinforcement learning"

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)