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: "bellwethers a baseline method for transfer learning"
  Abstract: Software analytics builds quality prediction models for software projects. Experience shows that (a) the more projects studied, the more varied are the conclusions; and (b) project managers lose faith in the results of software analytics if those results keep changing. To reduce this conclusion instability, we propose the use of "bellwethers": given N projects from a community the bellwether is the project whose data yields the best predictions on all others. The bellwethers offer a way to mitigate conclusion instability because conclusions about a community are stable as long as this bellwether continues as the best oracle. Bellwethers are also simple to discover (just wrap a for-loop around standard data miners). When compared to other transfer learning methods (TCA+, transfer Naive Bayes, value cognitive boosting), using just the bellwether data to construct a simple transfer learner yields comparable predictions. Further, bellwethers appear in many SE tasks such as defect prediction, effort estimation, and bad smell detection. We hence recommend using bellwethers as a baseline method for transfer learning against which future work should be compared

Connected through hyperedge HE-1 (induced by source paper "assessing practitioner beliefs about software defect prediction"):
  with members:
    - "we don t need another hero the impact of heroes on software development"
    - "are delayed issues harder to resolve revisiting cost to fix of defects throughout the lifecycle"

Connected through hyperedge HE-2 (induced by source paper "mat a simple yet strong baseline for identifying self admitted technical debt"):
  with members:
    - "towards good practices on building effective cnn baseline model for person re identification"
    - "simple baselines for human pose estimation and tracking"
    - "the effectiveness of instance normalization a strong baseline for single image dehazing"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "easy over hard a case study on deep learning"
    - "time series classification from scratch with deep neural networks a strong baseline"

Connected through hyperedge HE-3 (induced by source paper "whence to learn transferring knowledge in configurable systems using beetle"):
  with members:
    - "finding faster configurations using flash"
    - "micky a cheaper alternative for selecting cloud instances"
    - "scout an experienced guide to find the best cloud configuration"
    - "transfer learning for improving model predictions in highly configurable software"
    - "using bad learners to find good configurations"
    - "bestconfig tapping the performance potential of systems via automatic configuration tuning"
    - "faster discovery of faster system configurations with spectral learning"
    - "an uncertainty aware approach to optimal configuration of stream processing systems"

Connected through hyperedge HE-4 (induced by source paper "is ai different for se"):
  with members:
    - "how to dodge complex software analytics"
    - "a benchmark study on sentiment analysis for software engineering research"
    - "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"
    - "less is more minimizing code reorganization using xtree"
    - "better predictors for issue lifetime"

Connected through hyperedge HE-5 (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"
    - "we don t need another hero the impact of heroes on software development"
    - "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-6 (induced by source paper "learning general principles from hundreds of software projects"):
  with members:
    - "are delayed issues harder to resolve revisiting cost to fix of defects throughout the lifecycle"
    - "better predictors for issue lifetime"
    - "better data is better than better data miners benefits of tuning smote for defect prediction"
    - "a dissection of the test driven development process does it really matter to test first or to test last"
    - "defining and conceptualizing actionable insight a conceptual framework for decision centric analytics"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-7 (induced by source paper "revisiting heterogeneous defect prediction how far are we"):
  with members:
    - "an empirical study on software defect prediction with a simplified metric set"
    - "towards cross project defect prediction with imbalanced feature sets"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-8 (induced by source paper "assessing practitioner beliefs"):
  with members:
    - "we don t need another hero the impact of heroes on software development"
    - "are delayed issues harder to resolve revisiting cost to fix of defects throughout the lifecycle"

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)