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: "a benchmark study on sentiment analysis for software engineering research"
  Abstract: A recent research trend has emerged to identify developers' emotions, by applying sentiment analysis to the content of communication traces left in collaborative development environments. Trying to overcome the limitations posed by using off-the-shelf sentiment analysis tools, researchers recently started to develop their own tools for the software engineering domain. In this paper, we report a benchmark study to assess the performance and reliability of three sentiment analysis tools specifically customized for software engineering. Furthermore, we offer a reflection on the open challenges, as they emerge from a qualitative analysis of misclassified texts.1

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"
    - "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"
    - "steering output style and topic in neural response generation"

Connected through hyperedge HE-2 (induced by source paper "sentimoji an emoji powered learning approach for sentiment analysis in software engineering"):
  with members:
    - "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"
    - "recommending insightful comments for source code using crowdsourced knowledge"

Connected through hyperedge HE-3 (induced by source paper "is ai different for se"):
  with members:
    - "how to dodge complex software analytics"
    - "applications of psychological science for actionable analytics"
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "better software analytics via duo data mining algorithms using used by optimizers"
    - "bellwethers a baseline method for transfer learning"
    - "less is more minimizing code reorganization using xtree"
    - "better predictors for issue lifetime"

Connected through hyperedge HE-4 (induced by source paper "supervised sentiment classification with cnns for diverse se datasets"):
  with members:
    - "sentiment polarity detection for software development"
    - "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"

Connected through hyperedge HE-5 (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"
    - "need for sleep the impact of a night of sleep deprivation on novice developers performance"
    - "bootstrapping a lexicon for emotional arousal in software engineering"
    - "understanding the affect of developers theoretical background and guidelines for psychoempirical software engineering"
    - "are happy developers more productive"

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)