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: "function assistant a tool for nl querying of apis"
  Abstract: In this paper, we describe Function Assistant, a lightweight Python-based toolkit for querying and exploring source code repositories using natural language. The toolkit is designed to help end-users of a target API quickly find information about functions through high-level natural language queries and descriptions. For a given text query and background API, the tool finds candidate functions by performing a translation from the text to known representations in the API using the semantic parsing approach of Richardson and Kuhn (2017). Translations are automatically learned from example text-code pairs in example APIs. The toolkit includes features for building translation pipelines and query engines for arbitrary source code projects. To explore this last feature, we perform new experiments on 27 well-known Python projects hosted on Github.

Connected through hyperedge HE-1 (induced by source paper "polyglot semantic parsing in apis"):
  with members:
    - "a language for function signature representations"
    - "the code2text challenge text generation in source libraries"
    - "a parallel corpus of python functions and documentation strings for automated code documentation and code generation"
    - "neural machine translation for low resource languages"
    - "a survey of machine learning for big code and naturalness"
    - "neural semantic parsing over multiple knowledge bases"
    - "addressing the data sparsity issue in neural amr parsing"
    - "abstract syntax networks for code generation and semantic parsing"

Connected through hyperedge HE-2 (induced by source paper "a language for function signature representations"):
  with members:
    - "polyglot semantic parsing in apis"
    - "the code2text challenge text generation in source libraries"

Connected through hyperedge HE-3 (induced by source paper "the code2text challenge text generation in source libraries"):
  with members:
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "a parallel corpus of python functions and documentation strings for automated code documentation and code generation"
    - "a syntactic neural model for general purpose code generation"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "crowd sourcing nlg data pictures elicit better data"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

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)