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: "sentiment polarity detection for software development"
  Abstract: The role of sentiment analysis is increasingly emerging to study software developers' emotions by mining crowd-generated content within social software engineering tools. However, off-the-shelf sentiment analysis tools have been trained on non-technical domains and general-purpose social media, thus resulting in misclassifications of technical jargon and problem reports. Here, we present Senti4SD, a classifier specifically trained to support sentiment analysis in developers' communication channels. Senti4SD is trained and validated using a gold standard of Stack Overflow questions, answers, and comments manually annotated for sentiment polarity. It exploits a suite of both lexicon- and keyword-based features, as well as semantic features based on word embedding. With respect to a mainstream off-the-shelf tool, which we use as a baseline, Senti4SD reduces the misclassifications of neutral and positive posts as emotionally negative. To encourage replications, we release a lab package including the classifier, the word embedding space, and the gold standard with annotation guidelines.

Connected through hyperedge HE-1 (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"
    - "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"
    - "recommending insightful comments for source code using crowdsourced knowledge"

Connected through hyperedge HE-2 (induced by source paper "a large scale in depth analysis of developers personalities in the apache ecosystem"):
  with members:
    - "establishing personal trust based connections in distributed teams"
    - "on developers personality in large scale distributed projects the case of the apache ecosystem"
    - "a gold standard for emotion annotation in stack overflow"
    - "natural language or not nlon a package for software engineering text analysis pipeline"
    - "emotxt a toolkit for emotion recognition from text"
    - "a preliminary analysis on the effects of propensity to trust in distributed software development"
    - "25 tweets to know you a new model to predict personality with social media"
    - "links between the personalities styles and performance in computer programming"

Connected through hyperedge HE-3 (induced by source paper "on using machine learning to identify knowledge in api reference documentation"):
  with members:
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "natural language or not nlon a package for software engineering text analysis pipeline"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"
    - "easy over hard a case study on deep learning"
    - "an overview of gradient descent optimization algorithms"
    - "distributed representations of words and phrases and their compositionality"
    - "large scale multi label text classification revisiting neural networks"
    - "what should developers be aware of an empirical study on the directives of api documentation"

Connected through hyperedge HE-4 (induced by source paper "emtk the emotion mining toolkit"):
  with members:
    - "a gold standard for emotion annotation in stack overflow"
    - "emotxt a toolkit for emotion recognition from text"
    - "a computational approach to politeness with application to social factors"

Connected through hyperedge HE-5 (induced by source paper "release early release often and watch your users emotions"):
  with members:
    - "towards understanding and detecting fake reviews in app stores"
    - "on the emotion of users in app reviews"

Connected through hyperedge HE-6 (induced by source paper "sieve helping developers sift wheat from chaff via cross platform analysis"):
  with members:
    - "a practitioners guide to transfer learning for text classification using convolutional neural networks"
    - "strict information retrieval based search term identification for concept location"
    - "semantically enhanced software traceability using deep learning techniques"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "transfer learning for named entity recognition with neural networks"
    - "enriching word vectors with subword information"
    - "how transferable are neural networks in nlp applications"
    - "an insight into the unresolved questions at stack overflow"

Connected through hyperedge HE-7 (induced by source paper "a benchmark study on sentiment analysis for software engineering research"):
  with members:
    - "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"
    - "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 "supervised sentiment classification with cnns for diverse se datasets"):
  with members:
    - "a benchmark study on sentiment analysis for software engineering research"
    - "exploring the limits of language modeling"
    - "why is differential evolution better than grid search for tuning defect predictors"
    - "a c lstm neural network for text classification"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "smote synthetic minority over sampling technique"

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)