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: "neural enquirer learning to query tables with natural language"
  Abstract: We proposed Neural Enquirer as a neural network architecture to execute a natural language (NL) query on a knowledge-base (KB) for answers. Basically, Neural Enquirer finds the distributed representation of a query and then executes it on knowledge-base tables to obtain the answer as one of the values in the tables. Unlike similar efforts in end-to-end training of semantic parsers, Neural Enquirer is fully "neuralized": it not only gives distributional representation of the query and the knowledge-base, but also realizes the execution of compositional queries as a series of differentiable operations, with intermediate results (consisting of annotations of the tables at different levels) saved on multiple layers of memory. Neural Enquirer can be trained with gradient descent, with which not only the parameters of the controlling components and semantic parsing component, but also the embeddings of the tables and query words can be learned from scratch. The training can be done in an end-to-end fashion, but it can take stronger guidance, e.g., the step-by-step supervision for complicated queries, and benefit from it. Neural Enquirer is one step towards building neural network systems which seek to understand language by executing it on real-world. Our experiments show that Neural Enquirer can learn to execute fairly complicated NL queries on tables with rich structures.

Connected through hyperedge HE-1 (induced by source paper "a translate edit model for natural language question to sql query generation on multi relational healthcare data"):
  with members:
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "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"
    - "neural abstractive text summarization with sequence to sequence models a survey"
    - "bottom up abstractive summarization"
    - "improving text to sql evaluation methodology"

Connected through hyperedge HE-2 (induced by source paper "text2math end to end parsing text into math expressions"):
  with members:
    - "joint detection and location of english puns"
    - "translating a math word problem to an expression tree"
    - "semantic parsing with syntax and table aware sql generation"
    - "learning cross lingual distributed logical representations for semantic parsing"
    - "coarse to fine decoding for neural semantic parsing"
    - "a meaning based statistical english math word problem solver"
    - "mapping to declarative knowledge for word problem solving"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"

Connected through hyperedge HE-3 (induced by source paper "bridging the semantic gap with sql query logs in natural language interfaces to databases"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "an end to end neural natural language interface for databases"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"
    - "dataset for a neural natural language interface for databases nnlidb"
    - "learning a neural semantic parser from user feedback"
    - "language to logical form with neural attention"
    - "learning executable semantic parsers for natural language understanding"

Connected through hyperedge HE-4 (induced by source paper "semantic parsing with syntax and table aware sql generation"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "inferring and executing programs for visual reasoning"
    - "bidirectional attention for sql generation"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "learning to reason end to end module networks for visual question answering"
    - "weakly supervised semantic parsing with abstract examples"
    - "type and content driven synthesis of sql queries from natural language"

Connected through hyperedge HE-5 (induced by source paper "structvae tree structured latent variable models for semi supervised semantic parsing"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "neural amr sequence to sequence models for parsing and generation"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "learning a neural semantic parser from user feedback"
    - "abstract syntax networks for code generation and semantic parsing"
    - "learning structured natural language representations for semantic parsing"

Connected through hyperedge HE-6 (induced by source paper "transferable natural language interface to structured queries aided by adversarial generation"):
  with members:
    - "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"
    - "semantic parsing with syntax and table aware sql generation"
    - "a transfer learnable natural language interface for databases"
    - "coarse to fine decoding for neural semantic parsing"
    - "robust text to sql generation with execution guided decoding"
    - "syntax directed variational autoencoder for structured data"

Connected through hyperedge HE-7 (induced by source paper "exploring graph structured passage representation for multi hop reading comprehension with graph neural networks"):
  with members:
    - "joint training of candidate extraction and answer selection for reading comprehension"
    - "the web as a knowledge base for answering complex questions"
    - "n ary relation extraction using graph state lstm"
    - "neural models for reasoning over multiple mentions using coreference"
    - "deep contextualized word representations"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "a graph to sequence model for amr to text generation"
    - "evidence aggregation for answer re ranking in open domain question answering"

Connected through hyperedge HE-8 (induced by source paper "named entities troubling your neural methods build ne table a neural approach for handling named entities"):
  with members:
    - "deep contextualized word representations"
    - "improved neural relation detection for knowledge base question answering"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "pointing the unknown words"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "s mart novel tree based structured learning algorithms applied to tweet entity linking"
    - "end to end lstm based dialog control optimized with supervised and 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.AI(Artificial Intelligence)