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: "bootstrapping a lexicon for emotional arousal in software engineering"
  Abstract: Emotional arousal increases activation and performance but may also lead to burnout in software development. We present the first version of a Software Engineering Arousal lexicon (SEA) that is specifically designed to address the problem of emotional arousal in the software developer ecosystem. SEA is built using a bootstrapping approach that combines word embedding model trained on issue-tracking data and manual scoring of items in the lexicon. We show that our lexicon is able to differentiate between issue priorities, which are a source of emotional activation and then act as a proxy for arousal. The best performance is obtained by combining SEA (428 words) with a previously created general purpose lexicon by Warriner et al. (13,915 words) and it achieves Cohen's d effect sizes up to 0.5.

Connected through hyperedge HE-1 (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"
    - "on the unhappiness of software developers"
    - "mining valence arousal and dominance possibilities for detecting burnout and productivity"

Connected through hyperedge HE-2 (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"
    - "on the unhappiness of software developers"
    - "mining valence arousal and dominance possibilities for detecting burnout and productivity"

Connected through hyperedge HE-3 (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"
    - "emotxt a toolkit for emotion recognition from text"
    - "mining valence arousal and dominance possibilities for detecting burnout and productivity"
    - "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-4 (induced by source paper "sentiment polarity detection for software development"):
  with members:
    - "unhappy developers bad for themselves bad for process and bad for software product"
    - "mining valence arousal and dominance possibilities for detecting burnout and productivity"
    - "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"

Connected through hyperedge HE-5 (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"
    - "emotxt a toolkit for emotion recognition from text"
    - "recommending insightful comments for source code using crowdsourced knowledge"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "using experience sampling to link software repositories with emotions and work well being"):
  with members:
    - "a systematic review of productivity factors in software development"
    - "a benchmark study on sentiment analysis for software engineering research"
    - "need for sleep the impact of a night of sleep deprivation on novice developers performance"
    - "understanding the affect of developers theoretical background and guidelines for psychoempirical software engineering"
    - "are happy developers more productive"

Connected through hyperedge HE-7 (induced by source paper "a gold standard for emotion annotation in stack overflow"):
  with members:
    - "sentiment polarity detection for software development"
    - "how to ask for technical help evidence based guidelines for writing questions on stack overflow"
    - "emotxt a toolkit for emotion recognition from text"
    - "mining valence arousal and dominance possibilities for detecting burnout and productivity"

Connected through hyperedge HE-8 (induced by source paper "measuring lda topic stability from clusters of replicated runs"):
  with members:
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "how many topics stability analysis for topic models"

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)