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: "learning structured natural language representations for semantic parsing"
  Abstract: We introduce a neural semantic parser that converts natural language utterances to intermediate representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We obtain competitive results on various datasets. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones.

Connected through hyperedge HE-1 (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"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "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"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "jointly learning semantic parser and natural language generator via dual information maximization"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "coarse to fine decoding for neural semantic parsing"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "code2seq generating sequences from structured representations of code"
    - "triangle generative adversarial networks"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"

Connected through hyperedge HE-5 (induced by source paper "multi task learning for conversational question answering over a large scale knowledge base"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "coarse to fine decoding for neural semantic parsing"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"
    - "abstract syntax networks for code generation and semantic parsing"
    - "a syntactic neural model for general purpose code generation"
    - "language to logical form with neural attention"

Connected through hyperedge HE-6 (induced by source paper "a survey of cross lingual features for zero shot cross lingual semantic parsing"):
  with members:
    - "polyglot semantic role labeling"
    - "cross lingual semantic parsing"
    - "exploring neural methods for parsing discourse representation structures"
    - "evaluating scoped meaning representations"
    - "coarse to fine decoding for neural semantic parsing"
    - "neural networks for cross lingual negation scope detection"
    - "universal semantic parsing"
    - "cross lingual abstract meaning representation parsing"

Connected through hyperedge HE-7 (induced by source paper "a hybrid semantic parsing approach for tabular data analysis"):
  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"
    - "typesql knowledge based type aware neural text to sql generation"
    - "learning to compute word embeddings on the fly"
    - "universal semantic parsing"
    - "mimicking word embeddings using subword rnns"

Connected through hyperedge HE-8 (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"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "semantic parsing for task oriented dialog using hierarchical representations"
    - "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"

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)