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: "recommending insightful comments for source code using crowdsourced knowledge"
  Abstract: Recently, automatic code comment generation is proposed to facilitate program comprehension. Existing code comment generation techniques focus on describing the functionality of the source code. However, there are other aspects such as insights about quality or issues of the code, which are overlooked by earlier approaches. In this paper, we describe a mining approach that recommends insightful comments about the quality, deficiencies or scopes for further improvement of the source code. First, we conduct an exploratory study that motivates crowdsourced knowledge from Stack Overflow discussions as a potential resource for source code comment recommendation. Second, based on the findings from the exploratory study, we propose a heuristic-based technique for mining insightful comments from Stack Overflow Q & A site for source code comment recommendation. Experiments with 292 Stack Overflow code segments and 5,039 discussion comments show that our approach has a promising recall of 85.42%. We also conducted a complementary user study which confirms the accuracy and usefulness of the recommended comments.

Connected through hyperedge HE-1 (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"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "sentimoji an emoji powered learning approach for sentiment analysis in software engineering"):
  with members:
    - "a benchmark study on sentiment analysis for software engineering research"
    - "on the use of emoticons in open source software development"
    - "sentiment polarity detection for software development"
    - "through a gender lens learning usage patterns of emojis from large scale android users"
    - "a first look at emoji usage on github an empirical study"
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "attention is all you need"
    - "mining valence arousal and dominance possibilities for detecting burnout and productivity"

Connected through hyperedge HE-3 (induced by source paper "recommending comprehensive solutions for programming tasks by mining crowd knowledge"):
  with members:
    - "strict information retrieval based search term identification for concept location"
    - "effective reformulation of query for code search using crowdsourced knowledge and extra large data analytics"
    - "comment generation for source code state of the art challenges and opportunities"
    - "rack code search in the ide using crowdsourced knowledge"
    - "more accurate recommendations for method level changes"
    - "nlp2code code snippet content assist via natural language tasks"
    - "enriching word vectors with subword information"
    - "rack automatic api recommendation using crowdsourced knowledge"

Connected through hyperedge HE-4 (induced by source paper "generating summaries for methods of event driven programs an android case study"):
  with members:
    - "autofolding for source code summarization"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "a convolutional attention network for extreme summarization of source code"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "a benchmark study on sentiment analysis for software engineering research"):
  with members:
    - "sentiment polarity detection for software development"
    - "how to ask for technical help evidence based guidelines for writing questions on stack overflow"
    - "bootstrapping a lexicon for emotional arousal in software engineering"
    - "emotxt a toolkit for emotion recognition from text"
    - "mining valence arousal and dominance possibilities for detecting burnout and productivity"
    - "a computational approach to politeness with application to social factors"
    - "machine learning in automated text categorization"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-6 (induced by source paper "sentiment polarity detection for software development"):
  with members:
    - "bootstrapping a lexicon for emotional arousal in software engineering"
    - "unhappy developers bad for themselves bad for process and bad for software product"
    - "mining valence arousal and dominance possibilities for detecting burnout and productivity"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "a computational approach to politeness with application to social factors"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-7 (induced by source paper "how to ask for technical help evidence based guidelines for writing questions on stack overflow"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "bootstrapping a lexicon for emotional arousal in software engineering"
    - "emotxt a toolkit for emotion recognition from text"
    - "do feelings matter on the correlation of affects and the self assessed productivity in software engineering"
    - "how to ask for a favor a case study on the success of altruistic requests"
    - "the role of emotions in contributors activity a case study on the gentoo community"

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)