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: "polyglot semantic parsing in apis"
  Abstract: Traditional approaches to semantic parsing (SP) work by training individual models for each available parallel dataset of text-meaning pairs. In this paper, we explore the idea of polyglot semantic translation, or learning semantic parsing models that are trained on multiple datasets and natural languages. In particular, we focus on translating text to code signature representations using the software component datasets of Richardson and Kuhn (2017a,b). The advantage of such models is that they can be used for parsing a wide variety of input natural languages and output programming languages, or mixed input languages, using a single unified model. To facilitate modeling of this type, we develop a novel graph-based decoding framework that achieves state-of-the-art performance on the above datasets, and apply this method to two other benchmark SP tasks.

Connected through hyperedge HE-1 (induced by source paper "associating natural language comment and source code entities"):
  with members:
    - "structured neural summarization"
    - "wikiatomicedits a multilingual corpus of wikipedia edits for modeling language and discourse"
    - "the adverse effects of code duplication in machine learning models of code"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "typesql knowledge based type aware neural text to sql generation"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "retrieval based neural code generation"

Connected through hyperedge HE-2 (induced by source paper "partially supervised image captioning"):
  with members:
    - "neural baby talk"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "attention is all you need"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "zero shot learning the good the bad and the ugly"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-3 (induced by source paper "decoupling structure and lexicon for zero shot semantic parsing"):
  with members:
    - "weakly supervised semantic parsing with abstract examples"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "neural semantic parsing over multiple knowledge bases"
    - "learning a neural semantic parser from user feedback"
    - "transfer learning for neural semantic parsing"
    - "cross domain semantic parsing via paraphrasing"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "language to logical form with neural attention"

Connected through hyperedge HE-4 (induced by source paper "polyglot semantic role labeling"):
  with members:
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "massively multilingual word embeddings"
    - "neural semantic role labeling with dependency path embeddings"
    - "many languages one parser"
    - "training very deep networks"
    - "generating sequences with recurrent neural networks"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "a language for function signature representations"):
  with members:
    - "the code2text challenge text generation in source libraries"
    - "function assistant a tool for nl querying of apis"

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)