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: "coarse to fine decoding for neural semantic parsing"
  Abstract: Semantic parsing aims at mapping natural language utterances into structured meaning representations. In this work, we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. Given an input utterance, we first generate a rough sketch of its meaning, where low-level information (such as variable names and arguments) is glossed over. Then, we fill in missing details by taking into account the natural language input and the sketch itself. Experimental results on four datasets characteristic of different domains and meaning representations show that our approach consistently improves performance, achieving competitive results despite the use of relatively simple decoders.

Connected through hyperedge HE-1 (induced by source paper "structural language models of code"):
  with members:
    - "learning neurosymbolic generative models via program synthesis"
    - "learning to infer program sketches"
    - "learning programmatic idioms for scalable semantic parsing"
    - "write execute assess program synthesis with a repl"
    - "structured neural summarization"
    - "open vocabulary learning on source code with a graph structured cache"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"

Connected through hyperedge HE-2 (induced by source paper "learning to memorize in neural task oriented dialogue systems"):
  with members:
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "zero shot dialog generation with cross domain latent actions"
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "the natural language decathlon multitask learning as question answering"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "global locally self attentive dialogue state tracker"

Connected through hyperedge HE-3 (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"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"

Connected through hyperedge HE-4 (induced by source paper "meta module network for compositional visual reasoning"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "learning by abstraction the neural state machine"
    - "language conditioned graph networks for relational reasoning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "probabilistic neural symbolic models for interpretable visual question answering"

Connected through hyperedge HE-5 (induced by source paper "coarse grain fine grain coattention network for multi evidence question answering"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "efficient and robust question answering from minimal context over documents"
    - "neural models for reasoning over multiple mentions using coreference"
    - "global locally self attentive dialogue state tracker"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "exploring graph structured passage representation for multi hop reading comprehension with graph neural networks"

Connected through hyperedge HE-6 (induced by source paper "learning semantic parsers from denotations with latent structured alignments and abstract programs"):
  with members:
    - "learning to infer program sketches"
    - "learning to generalize from sparse and underspecified rewards"
    - "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"
    - "semantic parsing with syntax and table aware sql generation"
    - "allennlp a deep semantic natural language processing platform"
    - "decoupling structure and lexicon for zero shot semantic parsing"

Connected through hyperedge HE-7 (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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "semantic parsing with dual learning"):
  with members:
    - "agentgraph towards universal dialogue management with structured deep reinforcement learning"
    - "a hierarchical decoding model for spoken language understanding from unaligned data"
    - "question generation from sql queries improves neural semantic parsing"
    - "sequence to action end to end semantic graph generation for semantic parsing"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "dual supervised learning"
    - "weakly supervised semantic parsing with abstract examples"

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)