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 semantic parsing over multiple knowledge bases"
  Abstract: A fundamental challenge in developing semantic parsers is the paucity of strong supervision in the form of language utterances annotated with logical form. In this paper, we propose to exploit structural regularities in language in different domains, and train semantic parsers over multiple knowledge-bases (KBs), while sharing information across datasets. We find that we can substantially improve parsing accuracy by training a single sequence-to-sequence model over multiple KBs, when providing an encoding of the domain at decoding time. Our model achieves state-of-the-art performance on the Overnight dataset (containing eight domains), improves performance over a single KB baseline from 75.6% to 79.6%, while obtaining a 7x reduction in the number of model parameters.

Connected through hyperedge HE-1 (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"
    - "coarse to fine decoding for neural semantic parsing"
    - "dual supervised learning"

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 "spatialnli a spatial domain natural language interface to databases using spatial comprehension"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "encoding spatial relations from natural language"
    - "improving text to sql evaluation methodology"
    - "a transfer learnable natural language interface for databases"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "dataset for a neural natural language interface for databases nnlidb"
    - "learning a neural semantic parser from user feedback"
    - "abstract syntax networks for code generation and semantic parsing"

Connected through hyperedge HE-4 (induced by source paper "unified semantic parsing with weak supervision"):
  with members:
    - "sequence to action end to end semantic graph generation for semantic parsing"
    - "a teacher student framework for zero resource neural machine translation"
    - "weakly supervised semantic parsing with abstract examples"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "distral robust multitask reinforcement learning"
    - "learning from noisy labels with distillation"
    - "language to logical form with neural attention"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"

Connected through hyperedge HE-5 (induced by source paper "improving semantic parsing with neural generator reranker architecture"):
  with members:
    - "generating logical forms from graph representations of text and entities"
    - "self attention with relative position representations"
    - "sequence to action end to end semantic graph generation for semantic parsing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "macro grammars and holistic triggering for efficient semantic parsing"
    - "cross domain semantic parsing via paraphrasing"

Connected through hyperedge HE-6 (induced by source paper "look up and adapt a one shot semantic parser"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "zero shot transfer learning for semantic parsing"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "generating sentences by editing prototypes"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "cross domain semantic parsing via paraphrasing"
    - "language to logical form with neural attention"
    - "achieving human parity in conversational speech recognition"

Connected through hyperedge HE-7 (induced by source paper "zero shot semantic parsing for instructions"):
  with members:
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "quarel a dataset and models for answering questions about qualitative relationships"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "cross domain semantic parsing via paraphrasing"
    - "simpler context dependent logical forms via model projections"
    - "compositional semantic parsing on semi structured tables"
    - "lambda dependency based compositional semantics"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

Connected through hyperedge HE-8 (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"

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)