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: "personality is revealed during weekends towards data minimisation for smartphone based personality classification"
  Abstract: Previous literature has explored automatic personality modelling using smartphone data for its potential to personalise mobile services. Although passive modelling of personality removes the burden of completing lengthy questionnaires, the fact that such models typically require a few weeks or months of personal data can negatively impact user's engagement. %due to the delay until delivering personalised services as well as due to privacy concerns related to collecting a large amount of personal data. In this study, we explore the feasibility of reducing the duration of data collection in the context of personality classification. We found that only one or two weekends can suffice for achieving state-of-the-art accuracy between 66% and 71% for classifying the five personality traits. These results provide lessons for practicing "data minimisation" - a key principle of privacy laws.

Connected through hyperedge HE-1 (induced by source paper "modeling personality vs modeling personalidad in the wild mobile data analysis in five countries suggests cultural impact on personality models"):
  with members:
    - "aligning daily activities with personality towards a recommender system for improving wellbeing"
    - "when simpler data does not imply less information a study of user profiling scenarios with constrained view of mobile http s traffic"
    - "phone based metric as a predictor for basic personality traits"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-2 (induced by source paper "aligning daily activities with personality towards a recommender system for improving wellbeing"):
  with members:
    - "modeling personality vs modeling personalidad in the wild mobile data analysis in five countries suggests cultural impact on personality models"

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.HC(Human-Computer Interaction)