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: "mining valence arousal and dominance possibilities for detecting burnout and productivity"
  Abstract: Similar to other industries, the software engineering domain is plagued by psychological diseases such as burnout, which lead developers to lose interest, exhibit lower activity and/or feel powerless. Prevention is essential for such diseases, which in turn requires early identification of symptoms. The emotional dimensions of Valence, Arousal and Dominance (VAD) are able to derive a person’s interest (attraction), level of activation and perceived level of control for a particular situation from textual communication, such as emails. As an initial step towards identifying symptoms of productivity loss in software engineering, this paper explores the VAD metrics and their properties on 700,000 Jira issue reports containing over 2,000,000 comments, since issue reports keep track of a developer’s progress on addressing bugs or new features. Using a general-purpose lexicon of 14,000 English words with known VAD scores, our results show that issue reports of different type (e.g., Feature Request vs. Bug) have a fair variation of Valence, while increase in issue priority (e.g., from Minor to Critical) typically increases Arousal. Furthermore, we show that as an issue’s resolution time increases, so does the arousal of the individual the issue is assigned to. Finally, the resolution of an issue increases valence, especially for the issue Reporter and for quickly addressed issues. The existence ofsuch relations between VAD and issue report activities shows promise that text mining in the future could offer an alternative way for work health assessment surveys.

Connected through hyperedge HE-1 (induced by source paper "recognizing developers emotions while programming"):
  with members:
    - "a replication study on code comprehension and expertise using lightweight biometric sensors"
    - "model evaluation model selection and algorithm selection in machine learning"
    - "the impact of automated parameter optimization on defect prediction models"
    - "whom are you going to call determinants of mentions in github discussions"
    - "emotion detection using noninvasive low cost sensors"
    - "deep feelings a massive cross lingual study on the relation between emotions and virality"

Connected through hyperedge HE-2 (induced by source paper "time pressure in software engineering a systematic review"):
  with members:
    - "using experience sampling to link software repositories with emotions and work well being"
    - "reviewing literature on time pressure in software engineering and related professions computer assisted interdisciplinary literature review"
    - "bootstrapping a lexicon for emotional arousal in software engineering"
    - "on the unhappiness of software developers"

Connected through hyperedge HE-3 (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"
    - "recommending insightful comments for source code using crowdsourced knowledge"

Connected through hyperedge HE-4 (induced by source paper "time pressure in software engineering a systematic literature review"):
  with members:
    - "using experience sampling to link software repositories with emotions and work well being"
    - "reviewing literature on time pressure in software engineering and related professions computer assisted interdisciplinary literature review"
    - "bootstrapping a lexicon for emotional arousal in software engineering"
    - "on the unhappiness of software developers"

Connected through hyperedge HE-5 (induced by source paper "an empirical assessment on affective reactions of novice developers when applying test driven development"):
  with members:
    - "what happens when software developers are un happy"
    - "a dissection of the test driven development process does it really matter to test first or to test last"
    - "are happy developers more productive"

Connected through hyperedge HE-6 (induced by source paper "what happens when software developers are un happy"):
  with members:
    - "unhappy developers bad for themselves bad for process and bad for software product"
    - "consequences of unhappiness while developing software"
    - "on the unhappiness of software developers"
    - "understanding the affect of developers theoretical background and guidelines for psychoempirical software engineering"
    - "how do you feel developer an explanatory theory of the impact of affects on programming performance"
    - "the affect of software developers common misconceptions and measurements"
    - "do feelings matter on the correlation of affects and the self assessed productivity in software engineering"
    - "software developers moods emotions and performance"

Connected through hyperedge HE-7 (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"
    - "recommending insightful comments for source code using crowdsourced knowledge"
    - "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-8 (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"
    - "recommending insightful comments for source code using crowdsourced knowledge"
    - "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"

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)