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: "the code2text challenge text generation in source libraries"
  Abstract: We propose a new shared task for tactical data-to-text generation in the domain of source code libraries. Specifically, we focus on text generation of function descriptions from example software projects. Data is drawn from existing resources used for studying the related problem of semantic parser induction (Richardson and Kuhn, 2017b; Richardson and Kuhn, 2017a), and spans a wide variety of both natural languages and programming languages. In this paper, we describe these existing resources, which will serve as training and development data for the task, and discuss plans for building new independent test sets.

Connected through hyperedge HE-1 (induced by source paper "a neural model for generating natural language summaries of program subroutines"):
  with members:
    - "deep learning to detect redundant method comments"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "attention is all you need"
    - "a parallel corpus of python functions and documentation strings for automated code documentation and code generation"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "a survey of machine learning for big code and naturalness"
    - "a neural architecture for generating natural language descriptions from source code changes"

Connected through hyperedge HE-2 (induced by source paper "polyglot semantic parsing in apis"):
  with members:
    - "a language for function signature representations"
    - "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"
    - "function assistant a tool for nl querying of apis"
    - "abstract syntax networks for code generation and semantic parsing"

Connected through hyperedge HE-3 (induced by source paper "a language for function signature representations"):
  with members:
    - "polyglot semantic parsing in apis"
    - "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)