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: "guidelines for systematic mapping studies in security engineering"
  Abstract: Security engineering in the software lifecycle aims at protecting information and systems to guarantee confidentiality, integrity, and availability. As security engineering matures and the number of research papers grows, there is an increasing need for papers that summarize results and provide an overview of the area. A systematic mapping study "maps" a research area by classifying papers to identify which topics are well-studied and which need additional study. Therefore, systematic mapping studies are becoming increasingly important in security engineering. This chapter provides methodological support for systematic mapping studies in security engineering based on examples from published security engineering papers. Because security engineering is similar to software engineering in that it bridges research and practice, researchers can use the same basic systematic mapping process, as follows: (1) study planning, (2) searching for studies, (3) study selection, (4) study quality assessment, (5) data extraction, (6) data classification, (7) data analysis, and (8) reporting of results. We use published mapping studies to describe the tailoring of this process for security engineering. In addition to guidance on how to perform systematic mapping studies in security engineering, this chapter should increase awareness in the security engineering community of the need for additional mapping studies.

Connected through hyperedge HE-1 (induced by source paper "wikidata from a research perspective a systematic mapping study of wikidata"):
  with members:
    - "using word embeddings for visual data exploration with ontodia and wikidata"
    - "linking imagenet wordnet synsets with wikidata"
    - "wikidata vandalism detection the loganberry vandalism detector at wsdm cup 2017"
    - "overview of the wikidata vandalism detection task at wsdm cup 2017"
    - "learning to generate one sentence biographies from wikidata"
    - "predicting completeness in knowledge bases"
    - "building automated vandalism detection tools for wikidata"
    - "wikidata through the eyes of dbpedia"

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)