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: "we don t need another hero the impact of heroes on software development"
  Abstract: A software project has "Hero Developers" when 80% of contributions are delivered by 20% of the developers. Are such heroes a good idea? Are too many heroes bad for software quality? Is it better to have more/less heroes for different kinds of projects? To answer these questions, we studied 661 open source projects from Public open source software (OSS) Github and 171 projects from an Enterprise Github.   We find that hero projects are very common. In fact, as projects grow in size, nearly all projects become hero projects. These findings motivated us to look more closely at the effects of heroes on software development. Analysis shows that the frequency to close issues and bugs are not significantly affected by the presence of heroes or project type (Public or Enterprise). Similarly, the time needed to resolve an issue/bug/enhancement is not affected by heroes or project type. This is a surprising result since, before looking at the data, we expected that increasing heroes on a project will slow down how fast that project reacts to change. However, we do find a statistically significant association between heroes, project types, and enhancement resolution rates. Heroes do not affect enhancement resolution rates in Public projects. However, in Enterprise projects, heroes increase the rate at which projects complete enhancements.   In summary, our empirical results call for a revision of a long-held truism in software engineering. Software heroes are far more common and valuable than suggested by the literature, particularly for medium to large Enterprise developments. Organizations should reflect on better ways to find and retain more of these heroes.

Connected through hyperedge HE-1 (induced by source paper "the changing nature of computational science software"):
  with members:
    - "sequential model optimization for software process control"
    - "why software projects need heroes lessons learned from 1100 projects"
    - "are delayed issues harder to resolve revisiting cost to fix of defects throughout the lifecycle"

Connected through hyperedge HE-2 (induced by source paper "assessing practitioner beliefs about software defect prediction"):
  with members:
    - "bellwethers a baseline method for transfer learning"
    - "are delayed issues harder to resolve revisiting cost to fix of defects throughout the lifecycle"

Connected through hyperedge HE-3 (induced by source paper "enhanced labeling of issue reports with f 3 t"):
  with members:
    - "how to dodge complex software analytics"
    - "replication can improve prior results a github study of pull request acceptance"
    - "applications of psychological science for actionable analytics"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "finding better active learners for faster literature reviews"
    - "bellwethers a baseline method for transfer learning"
    - "easy over hard a case study on deep learning"
    - "why is differential evolution better than grid search for tuning defect predictors"

Connected through hyperedge HE-4 (induced by source paper "characterizing and understanding software developer networks in security development"):
  with members:
    - "a manually curated dataset of fixes to vulnerabilities of open source software"
    - "why software projects need heroes lessons learned from 1100 projects"
    - "developer prioritization in bug repositories"
    - "classifying developers into core and peripheral an empirical study on count and network metrics"
    - "evolutionary trends of developer coordination a network approach"
    - "categorizing bugs with social networks a case study on four open source software communities"
    - "the rise and fall of a central contributor dynamics of social organization and performance in the gentoo community"

Connected through hyperedge HE-5 (induced by source paper "security smells in infrastructure as code scripts"):
  with members:
    - "source code properties of defective infrastructure as code scripts"
    - "characterizing the influence of continuous integration empirical results from 250 open source and proprietary projects"
    - "what is the connection between issues bugs and enhancements lessons learned from 800 software projects"
    - "rehearsal a configuration verification tool for puppet"

Connected through hyperedge HE-6 (induced by source paper "assessing practitioner beliefs"):
  with members:
    - "bellwethers a baseline method for transfer learning"
    - "are delayed issues harder to resolve revisiting cost to fix of defects throughout the lifecycle"

Connected through hyperedge HE-7 (induced by source paper "bad smells in software analytics papers"):
  with members:
    - "the impact of automated parameter optimization on defect prediction models"
    - "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"
    - "data driven search based software engineering"
    - "revisiting unsupervised learning for defect prediction"
    - "what is the connection between issues bugs and enhancements lessons learned from 800 software projects"
    - "easy over hard a case study on deep learning"
    - "sampling as a baseline optimizer for search based software engineering"

Connected through hyperedge HE-8 (induced by source paper "can you explain that better comprehensible text analytics for se applications"):
  with members:
    - "applications of psychological science for actionable analytics"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "explainable software analytics"
    - "building better quality predictors using epsilon dominance"
    - "replicating and scaling up qualitative analysis using crowdsourcing a github based case study"
    - "the mythos of model interpretability"
    - "tuning for software analytics"

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)