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: "the effectiveness of traditional tools and computer aided technologies for health and safety training in the construction sector a systematic review"
  Abstract: For workers, the exposure to on-site hazards can result in fatalities and serious injuries. To improve safety outcomes, different approaches have been implemented for health and safety training in the construction sector, such as traditional tools and computer-aided technologies (e.g., serious games and virtual reality). However, the effectiveness of these approaches has been barely explored. In order to bridge this gap, a systematic review of existing studies was conducted. Unlike previous review studies in this field that focused on uncovering the technology characters and challenges, this study mainly evaluated the effectiveness of training using traditional tools and computer-aided technologies on the well-being of individuals. Measures of the effectiveness included knowledge acquisition, unsafe behaviour alteration, and injury rate reduction. Results indicated that: 1. the effectiveness of traditional tools is sufficiently supported by statistical evidence; and 2. the use of computer-aided technologies has evidence to support its effectiveness, but more solid evidence is required to support this statement. It was also found that the overall performance of computer-aided technologies is superior in several technical aspects compared to traditional tools, namely, representing actual workplace situations, providing text-free interfaces, having better user engagement, and being more cost-efficient. Finally, using the systematic review findings, a theoretical framework is proposed as a potential solution to help future research in this field systematically examine the effectiveness and usability of their approaches. This framework is theoretical in nature and requires further validation. A further study is therefore proposed to test and validate this framework.

Connected through hyperedge HE-1 (induced by source paper "the use of machine learning and big five personality taxonomy to predict construction workers safety behaviour"):
  with members:
    - "an immersive virtual reality serious game to enhance earthquake behavioral responses and post earthquake evacuation preparedness in buildings"
    - "immersive virtual reality serious games for evacuation training and research a systematic literature review"
    - "linear system identification in a nonlinear setting nonparametric analysis of the nonlinear distortions and their impact on the best linear approximation"
    - "cached sufficient statistics for efficient machine learning with large datasets"

Connected through hyperedge HE-2 (induced by source paper "an immersive virtual reality serious game to enhance earthquake behavioral responses and post earthquake evacuation preparedness in buildings"):
  with members:
    - "immersive virtual reality serious games for evacuation training and research a systematic literature review"
    - "prototyping virtual reality serious games for building earthquake preparedness the auckland city hospital case study"
    - "rapid 3d reconstruction of indoor environments to generate virtual reality serious games scenarios"

Connected through hyperedge HE-3 (induced by source paper "immersive virtual reality serious games for evacuation training and research a systematic literature review"):
  with members:
    - "prototyping virtual reality serious games for building earthquake preparedness the auckland city hospital case study"

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.CY(Computers and Society)