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: "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.

Connected through hyperedge HE-1 (induced by source paper "leveraging code generation to improve code retrieval and summarization via dual learning"):
  with members:
    - "coacor code annotation for code retrieval with reinforcement learning"
    - "dual supervised learning for natural language understanding and generation"
    - "jointly learning semantic parser and natural language generator via dual information maximization"
    - "same representation different attentions shareable sentence representation learning from multiple tasks"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "code2seq generating sequences from structured representations of code"
    - "dual supervised learning"
    - "a survey of machine learning for big code and naturalness"

Connected through hyperedge HE-2 (induced by source paper "graph4code a machine interpretable knowledge graph for code"):
  with members:
    - "improving natural language inference using external knowledge in the science questions domain"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "a survey of machine learning for big code and naturalness"
    - "the more you know using knowledge graphs for image classification"

Connected through hyperedge HE-3 (induced by source paper "coacor code annotation for code retrieval with reinforcement learning"):
  with members:
    - "comment generation for source code state of the art challenges and opportunities"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "neural network models for paraphrase identification semantic textual similarity natural language inference and question answering"
    - "visual question generation as dual task of visual question answering"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "a survey of machine learning for big code and naturalness"

Connected through hyperedge HE-4 (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"
    - "retrieval based neural code generation"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-5 (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"
    - "attention is all you need"
    - "the code2text challenge text generation in source libraries"
    - "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-6 (induced by source paper "codesearchnet challenge evaluating the state of semantic code search"):
  with members:
    - "coacor code annotation for code retrieval with reinforcement learning"
    - "when deep learning met code search"
    - "structured neural summarization"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "the adverse effects of code duplication in machine learning models of code"
    - "mapping language to code in programmatic context"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "code2seq generating sequences from structured representations of code"

Connected through hyperedge HE-7 (induced by source paper "a survey on semantic parsing"):
  with members:
    - "learning programmatic idioms for scalable semantic parsing"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "mapping language to code in programmatic context"
    - "semantic parsing with syntax and table aware sql generation"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"

Connected through hyperedge HE-8 (induced by source paper "learning to mine aligned code and natural language pairs from stack overflow"):
  with members:
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "a parallel corpus of python functions and documentation strings for automated code documentation and code generation"
    - "abstract syntax networks for code generation and semantic parsing"
    - "a syntactic neural model for general purpose code generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "from query to usable code an analysis of stack overflow code snippets"
    - "neural generation of regular expressions from natural language with minimal domain knowledge"

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)