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 "social ties and emotions evidence from social media"):
  Abstract: The social connections, or ties, individuals create affect their life outcomes, for example, by providing novel information that leads to new jobs or career opportunities. A host of socioeconomic and cognitive factors are believed to affect social interactions, but few of these factors have been empirically validated. In this research work, we extracted a large corpus of data from a popular social media platform that consists of geo-referenced messages, or tweets, posted from a major US metropolitan area. We linked these tweets to US Census data through their locations. This allowed us to measure emotions expressed in tweets posted from a specific area, and also use that area's socioeconomic and demographic characteristics in the analysis. We extracted the structure of social interactions from the people mentioned in tweets from that area. We find that at an aggregate level, areas where social media users engage in stronger, less diverse online social interactions are those where they express more negative emotions, like sadness and anger. With respect to demographics, these areas have larger numbers of Hispanic residents, lower mean household income, and lower education levels. Conversely, areas with weaker, more diverse online interactions are associated with happier, more positive feelings and also have better educated, younger and higher-earning residents. Our work highlights the value of linking social media data to traditional data sources, such as US Census, to drive novel analysis of online behavior.

Includes members:
  Member "measuring urban deprivation from user generated content":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploiting population activity dynamics to predict urban epidemiological incidence")
  Member "user effort and network structure mediate access to information in networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "information is not a virus and other consequences of human cognitive limits")
  Member "cultures in community question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "why the world reads wikipedia beyond english speakers")
  Member "sentiment cascades in the 15m movement":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "integrating sentiment and social structure to determine preference alignments the irish marriage referendum")
  Member "big questions for social media big data representativeness validity and other methodological pitfalls":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bias in data driven ai systems an introductory survey")
  Member "comparing and combining sentiment analysis methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "modeling spatiotemporal factors associated with sentiment on twitter synthesis and suggestions for improving the identification of localized deviations")
  Member "positive words carry less information than negative words":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the qwerty effect on the web how typing shapes the meaning of words in online human computer interaction")

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)