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: "automatically generating commit messages from diffs using neural machine translation"
  Abstract: Commit messages are a valuable resource in comprehension of software evolution, since they provide a record of changes such as feature additions and bug repairs. Unfortunately, programmers often neglect to write good commit messages. Different techniques have been proposed to help programmers by automatically writing these messages. These techniques are effective at describing what changed, but are often verbose and lack context for understanding the rationale behind a change. In contrast, humans write messages that are short and summarize the high level rationale. In this paper, we adapt Neural Machine Translation (NMT) to automatically "translate" diffs into commit messages. We trained an NMT algorithm using a corpus of diffs and human-written commit messages from the top 1k Github projects. We designed a filter to help ensure that we only trained the algorithm on higher-quality commit messages. Our evaluation uncovered a pattern in which the messages we generate tend to be either very high or very low quality. Therefore, we created a quality-assurance filter to detect cases in which we are unable to produce good messages, and return a warning instead.

Connected through hyperedge HE-1 (induced by source paper "automating app review response generation"):
  with members:
    - "what you say and how you say it joint modeling of topics and discourse in microblog conversations"
    - "a benchmark study on sentiment analysis for software engineering research"
    - "four commentaries on the use of students and professionals in empirical software engineering experiments"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "topic memory networks for short text classification"
    - "essmart way to manage user requests"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "steering output style and topic in neural response generation"

Connected through hyperedge HE-2 (induced by source paper "cc2vec distributed representations of code changes"):
  with members:
    - "patchnet hierarchical deep learning based stable patch identification for the linux kernel"
    - "semantic source code models using identifier embeddings"
    - "a neural model for generating natural language summaries of program subroutines"
    - "import2vec learning embeddings for software libraries"
    - "code2vec learning distributed representations of code"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "code2seq generating sequences from structured representations of code"

Connected through hyperedge HE-3 (induced by source paper "on learning meaningful assert statements for unit test cases"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "a neural model for generating natural language summaries of program subroutines"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "learning how to mutate source code from bug fixes"
    - "learning to generate corrective patches using neural machine translation"
    - "six challenges for neural machine translation"
    - "deep api learning"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "the effectiveness of supervised machine learning algorithms in predicting software refactoring"):
  with members:
    - "do design metrics capture developers perception of quality an empirical study on self affirmed refactoring activities"
    - "code2vec learning distributed representations of code"
    - "a survey of machine learning for big code and naturalness"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (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"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "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"
    - "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-6 (induced by source paper "atom commit message generation based on abstract syntax tree and hybrid ranking"):
  with members:
    - "a hybrid retrieval generation neural conversation model"
    - "generating commit messages from git diffs"
    - "devign effective vulnerability identification by learning comprehensive program semantics via graph neural networks"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "code2seq generating sequences from structured representations of code"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "a survey of automatic generation of source code comments algorithms and techniques"):
  with members:
    - "automatic generation of text descriptive comments for code blocks"
    - "deep reinforcement learning for sequence to sequence models"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "attention is all you need"
    - "code attention translating code to comments by exploiting domain features"
    - "a survey of machine learning for big code and naturalness"
    - "convolutional sequence to sequence learning"
    - "a convolutional attention network for extreme summarization of source code"

Connected through hyperedge HE-8 (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"
    - "the code2text challenge text generation in source libraries"
    - "a parallel corpus of python functions and documentation strings for automated code documentation and code generation"
    - "a survey of machine learning for big code and naturalness"
    - "a neural architecture for generating natural language descriptions from source code changes"

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.SE(Software Engineering)