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: "transfer learning for neural semantic parsing"
  Abstract: The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL). One of the constraints that limits full exploration of deep learning technologies for semantic parsing is the lack of sufficient annotation training data. In this paper, we propose using sequence-to-sequence in a multi-task setup for semantic parsing with a focus on transfer learning. We explore three multi-task architectures for sequence-to-sequence modeling and compare their performance with an independently trained model. Our experiments show that the multi-task setup aids transfer learning from an auxiliary task with large labeled data to a target task with smaller labeled data. We see absolute accuracy gains ranging from 1.0% to 4.4% in our in- house data set, and we also see good gains ranging from 2.5% to 7.0% on the ATIS semantic parsing tasks with syntactic and semantic auxiliary tasks.

Connected through hyperedge HE-1 (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"
    - "neural semantic parsing over multiple knowledge bases"
    - "learning a neural semantic parser from user feedback"

Connected through hyperedge HE-2 (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"
    - "learning structured natural language representations for 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-3 (induced by source paper "multi task networks with universe group and task feature learning"):
  with members:
    - "soft layer specific multi task summarization with entailment and question generation"
    - "transfer and multi task learning for noun noun compound interpretation"
    - "aspect augmented adversarial networks for domain adaptation"
    - "adversarial multi task learning for text classification"
    - "multi task label embedding for text classification"
    - "recurrent neural network for text classification with multi task learning"
    - "neural architectures for named entity recognition"
    - "domain separation networks"

Connected through hyperedge HE-4 (induced by source paper "compositional pre training for neural semantic parsing"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "a syntactic neural model for general purpose code generation"
    - "unsupervised pretraining for sequence to sequence learning"
    - "data recombination for neural semantic parsing"
    - "sequence to sequence learning with neural networks"
    - "lambda dependency based compositional semantics"
    - "learning dependency based compositional semantics"

Connected through hyperedge HE-5 (induced by source paper "structvae tree structured latent variable models for semi supervised semantic parsing"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "neural amr sequence to sequence models for parsing and generation"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "learning a neural semantic parser from user feedback"
    - "abstract syntax networks for code generation and semantic parsing"
    - "learning structured natural language representations for semantic parsing"

Connected through hyperedge HE-6 (induced by source paper "dynamic transfer learning for named entity recognition"):
  with members:
    - "joint entity extraction and assertion detection for clinical text"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "the natural language decathlon multitask learning as question answering"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "dynamic multi level multi task learning for sentence simplification"

Connected through hyperedge HE-7 (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"
    - "sequence to action end to end semantic graph generation for semantic parsing"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "multitask parsing across semantic representations"):
  with members:
    - "learning joint semantic parsers from disjoint data"
    - "a transition based directed acyclic graph parser for ucca"
    - "neural amr sequence to sequence models for parsing and generation"
    - "improving generalization performance by switching from adam to sgd"
    - "robust incremental neural semantic graph parsing"
    - "deep multitask learning for semantic dependency parsing"
    - "exploiting multi typed treebanks for parsing with deep multi task learning"
    - "enriching word vectors with subword information"

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)