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: "decoupling structure and lexicon for zero shot semantic parsing"
  Abstract: Building a semantic parser quickly in a new domain is a fundamental challenge for conversational interfaces, as current semantic parsers require expensive supervision and lack the ability to generalize to new domains. In this paper, we introduce a zero-shot approach to semantic parsing that can parse utterances in unseen domains while only being trained on examples in other source domains. First, we map an utterance to an abstract, domain-independent, logical form that represents the structure of the logical form, but contains slots instead of KB constants. Then, we replace slots with KB constants via lexical alignment scores and global inference. Our model reaches an average accuracy of 53.4% on 7 domains in the Overnight dataset, substantially better than other zero-shot baselines, and performs as good as a parser trained on over 30% of the target domain examples.

Connected through hyperedge HE-1 (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"
    - "memory augmented policy optimization for program synthesis and semantic parsing"

Connected through hyperedge HE-2 (induced by source paper "don t paraphrase detect rapid and effective data collection for semantic parsing"):
  with members:
    - "learning to learn semantic parsers from natural language supervision"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "end to end open domain question answering with"
    - "practical semantic parsing for spoken language understanding"
    - "building a neural semantic parser from a domain ontology"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "building a conversational agent overnight with dialogue self play"

Connected through hyperedge HE-3 (induced by source paper "coupling retrieval and meta learning for context dependent semantic parsing"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "dictionary guided editing networks for paraphrase generation"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "response generation by context aware prototype editing"
    - "mapping language to code in programmatic context"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "spherical latent spaces for stable variational autoencoders"
    - "sequence to action end to end semantic graph generation for semantic parsing"

Connected through hyperedge HE-4 (induced by source paper "robust zero shot cross domain slot filling with example values"):
  with members:
    - "zero shot adaptive transfer for conversational language understanding"
    - "multi source domain adaptation with mixture of experts"
    - "contextual slot carryover for disparate schemas"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "fast and scalable expansion of natural language understanding functionality for intelligent agents"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"
    - "zero shot user intent detection via capsule neural networks"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "grammar based neural text to sql generation"):
  with members:
    - "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"
    - "improving text to sql evaluation methodology"
    - "mapping language to code in programmatic context"
    - "allennlp a deep semantic natural language processing platform"
    - "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-7 (induced by source paper "practical semantic parsing for spoken language understanding"):
  with members:
    - "parsing coordination for spoken language understanding"
    - "an efficient approach to encoding context for spoken language understanding"
    - "semantic parsing for task oriented dialog using hierarchical representations"
    - "learning structured natural language representations for semantic parsing"
    - "transfer learning for neural semantic parsing"
    - "an overview of multi task learning in deep neural networks"
    - "cross domain semantic parsing via paraphrasing"
    - "pointing the unknown words"

Connected through hyperedge HE-8 (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"
    - "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"
    - "robust text to sql generation with execution guided decoding"

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)