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: "sequence to action end to end semantic graph generation for semantic parsing"
  Abstract: This paper proposes a neural semantic parsing approach -- Sequence-to-Action, which models semantic parsing as an end-to-end semantic graph generation process. Our method simultaneously leverages the advantages from two recent promising directions of semantic parsing. Firstly, our model uses a semantic graph to represent the meaning of a sentence, which has a tight-coupling with knowledge bases. Secondly, by leveraging the powerful representation learning and prediction ability of neural network models, we propose a RNN model which can effectively map sentences to action sequences for semantic graph generation. Experiments show that our method achieves state-of-the-art performance on OVERNIGHT dataset and gets competitive performance on GEO and ATIS datasets.

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"
    - "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"
    - "weakly supervised semantic parsing with abstract examples"

Connected through hyperedge HE-2 (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"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "spherical latent spaces for stable variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "a sketch based system for semantic parsing"):
  with members:
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "bert for joint intent classification and slot filling"
    - "transferable natural language interface to structured queries aided by adversarial generation"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "semantic parsing with syntax and table aware sql generation"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "coarse to fine decoding for neural semantic parsing"

Connected through hyperedge HE-4 (induced by source paper "unified semantic parsing with weak supervision"):
  with members:
    - "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"
    - "neural semantic parsing over multiple knowledge bases"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "neural semantic parsing over multiple knowledge bases"
    - "macro grammars and holistic triggering for efficient semantic parsing"
    - "cross domain semantic parsing via paraphrasing"

Connected through hyperedge HE-6 (induced by source paper "semantic graph parsing with recurrent neural network dag grammars"):
  with members:
    - "exploring neural methods for parsing discourse representation structures"
    - "learning deep generative models of graphs"
    - "amr dependency parsing with a typed semantic algebra"
    - "amr parsing as graph prediction with latent alignment"
    - "towards universal semantic tagging"
    - "robust incremental neural semantic graph parsing"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "treegen a tree based transformer architecture for code generation"):
  with members:
    - "a grammar based structural cnn decoder for code generation"
    - "abstract syntax networks for code generation and semantic parsing"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "building a neural semantic parser from a domain ontology"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "semantic parsing for task oriented dialog using hierarchical representations"
    - "universal semantic parsing"
    - "making neural programming architectures generalize via recursion"
    - "neural semantic parsing over multiple knowledge bases"
    - "learning structured natural language representations for semantic parsing"
    - "transfer learning for neural semantic parsing"

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)