human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "staqc a systematically mined question code dataset from stack overflow"):
  Abstract: Stack Overflow has been a great source of natural language questions and their code solutions (i.e., question-code pairs), which are critical for many tasks including retrieving code snippets based on a keyword query and code annotation using natural language. In most existing research, question-code pairs were collected heuristically and tend to have low quality: For example, given a question post and its answer post, one simply pairs the question title with every code snippet (if any) in the answer post, which is problematic since a code snippet may not serve as a solution to the question. In this paper, we investigate a new problem of systematically mining question-code pairs from Stack Overflow (in contrast to heuristically collecting them). The task is formulated as predicting whether or not a code snippet is a standalone solution to a question. We propose a novel Bi-view Hierarchical Neural Network which can capture both the programming content and the textual context of a code snippet (i.e., two views) to make a prediction. On two manually annotated datasets in Python and SQL domain, our framework substantially outperforms heuristic methods with at least 15% higher F1 and accuracy. Furthermore, we obtain StaQC (Stack Overflow Question-Code pairs), the largest dataset to date of 148K Python and 120K SQL question-code pairs, automatically mined from SO using our framework. Under various case studies, we demonstrate that StaQC can greatly help develop data-hungry models for associating natural language with programming language.

Includes members:
  Member "a parallel corpus of python functions and documentation strings for automated code documentation and code generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trans 3 a transformer based framework for unifying code summarization and code search")
  Member "abstract syntax networks for code generation and semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "schema2qa answering complex queries on the structured web with a neural model")
  Member "a neural architecture for generating natural language descriptions from source code changes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "coacor code annotation for code retrieval with reinforcement learning")
  Member "a syntactic neural model for general purpose code generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploring neural models for parsing natural language into first order logic")
  Member "nlp2code code snippet content assist via natural language tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "recommending comprehensive solutions for programming tasks by mining crowd knowledge")
  Member "from query to usable code an analysis of stack overflow code snippets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "code duplication on stack overflow")
  Member "latent predictor networks for code generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi modal attention network learning for semantic source code retrieval")
  Member "a convolutional attention network for extreme summarization of source code":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "big code big vocabulary open vocabulary models for source code")

Question: Please tell me which class the source paper 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)