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: "landscaping systematic mapping studies in software engineering a tertiary study"
  Abstract: Context: A number of Systematic Mapping Studies (SMSs) that cover Software Engineering (SE) are reported in literature. Tertiary studies synthesize the secondary studies to provide a holistic view of an area. Objectives: We synthesize SMSs in SE to provide insights into existing SE areas and to investigate the trends and quality of SMSs. Methodology: We use Systematic Literature Review protocol to analyze and map the SMSs in SE, till August 2017, to SE Body of Knowledge (SWEBOK). Results: We analyze 210 SMSs and results show that: (1) Software design and construction are most active areas in SE; (2) Some areas lack SMSs, including mathematical foundations, software configuration management, and SE tools; (3) The quality of SMSs is improving with time; (4) SMSs in journals have higher quality than SMSs in conferences and are cited more often; (5) Low quality in SMSs can be attributed to a lack of quality assessment in SMSs and not reporting information about the primary studies. Conclusion: There is a potential for more SMSs in some SE areas. A number of SMSs do not provide the required information for an SMS, which leads to a low quality score.

Connected through hyperedge HE-1 (induced by source paper "a systematic mapping study on testing of machine learning programs"):
  with members:
    - "formal security analysis of neural networks using symbolic intervals"
    - "towards dependability metrics for neural networks"
    - "testing scientific software a systematic literature review"
    - "efficient formal safety analysis of neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "feature guided black box safety testing of deep neural networks"
    - "safety verification of deep neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "a systematic literature review of test breakage prevention and repair techniques"):
  with members:
    - "testing scientific software a systematic literature review"

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)