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: "bad smells in software analytics papers"
  Abstract: CONTEXT: There has been a rapid growth in the use of data analytics to underpin evidence-based software engineering. However the combination of complex techniques, diverse reporting standards and complex underlying phenomena are causing some concern as to the reliability of studies. #R##N#OBJECTIVE: Our goal is to provide guidance for producers and consumers of software analytics studies (computational experiments and correlation studies). #R##N#METHOD: We propose using "bad smells", i.e. surface indications of deeper problems and popular in the agile software community and consider how they may be manifest in software analytics studies. #R##N#RESULTS: We provide a list of 11 "bad smells" in decreasing order of severity and show their impact by examples. #R##N#CONCLUSIONS: We should encourage more debate on what constitutes a `valid' study (so we expect our list will mature over time).

Connected through hyperedge HE-1 (induced by source paper "better security bug report classification via hyperparameter optimization"):
  with members:
    - "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"
    - "hyperparameter optimization for effort estimation"
    - "less is more minimizing code reorganization using xtree"
    - "better data is better than better data miners benefits of tuning smote for defect prediction"
    - "a brief survey of text mining classification clustering and extraction techniques"
    - "why is differential evolution better than grid search for tuning defect predictors"
    - "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)