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 "evaluation of biases in self reported demographic and psychometric information traditional versus facebook based surveys"):
  Abstract: Social media in scientific research offer a unique digital observatory of human behaviours and hence great opportunities to conduct research at large scale answering complex sociodemographic questions. We focus on the identification and assessment of biases in social media administered surveys. This study aims to shed light on population, self-selection and behavioural biases, empirically comparing the consistency between self-reported information collected traditionally versus social media administered questionnaires, including demographic and psychometric attributes. We engaged a demographically representative cohort of young adults in Italy (approximately 4,000 participants) in taking a traditionally administered online survey and then, after one year, we invited them to use our ad hoc Facebook application (988 accepted) where they filled in part of the initial survey. We assess the statistically significant differences indicating population, self-selection, and behavioural biases due to the different context in which the questionnaire is administered. Our findings suggest that surveys administered on Facebook do not exhibit major biases with respect to traditionally administered surveys neither in terms of demographics, nor personality traits. Loyalty, authority, and social binding values were higher in the Facebook platform, probably due to the platform's intrinsic social character. We conclude, that Facebook apps are valid research tools for administering demographic and psychometric surveys provided that the entailed biases are taken into consideration. We contribute to the characterisation of Facebook apps as a valid scientific tool to administer demographic and psychometric surveys, and to the assessment of population, self-selection, and behavioural biases in the collected data.

Includes members:
  Member "predicting demographics moral foundations and human values from digital behaviours":
    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 "studying migrant assimilation through facebook interests":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "facebook ads as a demographic tool to measure the urban rural divide")
  Member "using facebook ads audiences for global lifestyle disease surveillance promises and limitations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "facebook ads as a demographic tool to measure the urban rural divide")
  Member "geo located twitter as proxy for global mobility patterns":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transfer learning from an auxiliary discriminative task for unsupervised anomaly detection")
  Member "processing social media messages in mass emergency a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "localized flood detectionwith minimal labeled social media data using transfer learning")
  Member "big questions for social media big data representativeness validity and other methodological pitfalls":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bias in data driven ai systems an introductory survey")

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)