human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "predicting demographics moral foundations and human values from digital behaviours"):
  Abstract: Abstract   Personal electronic devices including smartphones give access to behavioural signals that can be used to learn about the characteristics and preferences of individuals. In this study, we explore the connection between demographic and psychological attributes and the digital behavioural records, for a cohort of 7633 people, closely representative of the US population with respect to gender, age, geographical distribution, education, and income. Along with the demographic data, we collected self-reported assessments on validated psychometric questionnaires for moral traits and basic human values, and combined this information with passively collected multi-modal digital data from web browsing behaviour and smartphone usage. A machine learning framework was then designed to infer both the demographic and psychological attributes from the behavioural data. In a cross-validated setting, our models predicted demographic attributes with good accuracy as measured by the weighted AUROC score (Area Under the Receiver Operating Characteristic), but were less performant for the moral traits and human values. These results call for further investigation, since they are still far from unveiling individuals' psychological fabric. This connection, along with the most predictive features that we provide for each attribute, might prove useful for designing personalised services, communication strategies, and interventions, and can be used to sketch a portrait of people with similar worldview.

Includes members:
  Member "studying migrant assimilation through facebook interests":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "facebook ads as a demographic tool to measure the urban rural divide")
  Member "acquiring background knowledge to improve moral value prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sampling the news producers a large news and feature data set for the study of the complex media landscape")
  Member "how well can machine learning predict demographics of social media users":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "going beyond accuracy estimating homophily in social networks using predictions")
  Member "stereotyping and bias in the flickr30k dataset":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithmic fairness")
  Member "you are what apps you use demographic prediction based on user s apps":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "predicting multiple demographic attributes with task specific embedding transformation and attention network")
  Member "a survey on measuring indirect discrimination in machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fairsight visual analytics for fairness in decision making")
  Member "using deep learning to predict demographics from mobile phone metadata":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the role of gender in social network organization")
  Member "fairness through awareness":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "algorithmic fairness")

Question: Please tell me which class the source paper 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)