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: "what we don t know about spreadsheet errors today the facts why we don t believe them and what we need to do"
  Abstract: Research on spreadsheet errors is substantial, compelling, and unanimous. It has three simple conclusions. The first is that spreadsheet errors are rare on a per-cell basis, but in large programs, at least one incorrect bottom-line value is very likely to be present. The second is that errors are extremely difficult to detect and correct. The third is that spreadsheet developers and corporations are highly overconfident in the accuracy of their spreadsheets. The disconnect between the first two conclusions and the third appears to be due to the way human cognition works. Most importantly, we are aware of very few of the errors we make. In addition, while we are proudly aware of errors that we fix, we have no idea of how many remain, but like Little Jack Horner we are impressed with our ability to ferret out errors. This paper reviews human cognition processes and shows first that humans cannot be error free no matter how hard they try, and second that our intuition about errors and how we can reduce them is based on appallingly bad knowledge. This paper argues that we should reject any prescription for reducing errors that has not been rigorously proven safe and effective. The paper also argues that our biggest need, based on empirical data, is to do massively more testing than we do now. It suggests that the code inspection methodology developed in software development is likely to apply very well to spreadsheet inspection.

Connected through hyperedge HE-1 (induced by source paper "the future of spreadsheets in the big data era"):
  with members:
    - "multidisciplinary engineering models methodology and case study in spreadsheet analytics"
    - "governance of spreadsheets through spreadsheet change reviews"
    - "the detection of human spreadsheet errors by humans versus inspection auditing software"
    - "spreadsheets grow up three spreadsheet engineering methodologies for large financial planning models"
    - "reducing the risk of spreadsheet usage a case study"
    - "spreadsheets and the financial collapse"

Connected through hyperedge HE-2 (induced by source paper "mitigating spreadsheet model risk with python open source infrastructure"):
  with members:
    - "practical challenges with spreadsheet auditing tools"

Connected through hyperedge HE-3 (induced by source paper "edu edition spreadsheet competency framework"):
  with members:
    - "teaching methods are erroneous approaches which lead to erroneous end user computing"

Connected through hyperedge HE-4 (induced by source paper "automated refactoring of nested if formulae in spreadsheets"):
  with members:
    - "a conceptual model for measuring the complexity of spreadsheets"
    - "enron versus euses a comparison of two spreadsheet corpora"
    - "the detection of human spreadsheet errors by humans versus inspection auditing software"
    - "classification of spreadsheet errors"

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)