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: "towards automatic generation of short summaries of commits"
  Abstract: Committing to a version control system means submitting a software change to the system. Each commit can have a message to describe the submission. Several approaches have been proposed to automatically generate the content of such messages. However, the quality of the automatically generated messages falls far short of what humans write. In studying the differences between auto-generated and human-written messages, we found that 82% of the human-written messages have only one sentence, while the automatically generated messages often have multiple lines. Furthermore, we found that the commit messages often begin with a verb followed by an direct object. This finding inspired us to use a "verb+object" format in this paper to generate short commit summaries. We split the approach into two parts: verb generation and object generation. As our first try, we trained a classifier to classify a diff to a verb. We are seeking feedback from the community before we continue to work on generating direct objects for the commits.

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"
    - "automatically generating commit messages from diffs using neural machine translation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "generating commit messages from git diffs"):
  with members:
    - "code2vec learning distributed representations of code"
    - "code2seq generating sequences from structured representations of code"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "a survey of machine learning for big code and naturalness"
    - "nematus a toolkit for neural machine translation"
    - "a neural architecture for generating natural language descriptions from source code changes"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "automatically generating commit messages from diffs using neural machine translation"):
  with members:
    - "semantically enhanced software traceability using deep learning techniques"
    - "opennmt open source toolkit for neural machine translation"
    - "a syntactic neural model for general purpose code generation"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "latent predictor networks for code generation"
    - "deep api learning"
    - "edinburgh neural machine translation systems for wmt 16"
    - "linguistic input features improve neural machine translation"

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)