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 "when sheep shop measuring herding effects in product ratings with natural experiments"):
  Abstract: As online shopping becomes ever more prevalent, customers rely increasingly on product rating websites for making purchase decisions. The reliability of online ratings, however, is potentially compromised by the so-called herding effect: when rating a product, customers may be biased to follow other customers’ previous ratings of the same product. This is problematic because it skews long-term customer perception through haphazard early ratings. The study of herding poses important methodological challenges. Observational studies are impeded by the lack of counterfactuals: simply correlating early with subsequent ratings is insufficient because we cannot know what the subsequent ratings would have looked like had the first ratings been different. Experimental studies are rarely an option because either they manipulate real customers’ attitudes toward real products, or they examine lab settings that might differ fundamentally from real settings. The methodology introduced here exploits a situation that comes close to an experiment, although it is purely observational – a natural experiment. Our key methodological device consists in studying the same product on two separate rating sites, focusing on products that received a high first rating on one site, and a low first rating on the other. This largely controls for confounds such as a product’s inherent quality, advertising, and producer identity, and lets us isolate the effect of the first rating on subsequent ratings. In a case study, we focus on beers as products and jointly study two beer rating sites, but our method applies to any pair of sites across which products can be matched. We find clear evidence of herding in beer ratings. For instance, if a beer receives a very high first rating, its second rating is on average half a standard deviation higher, compared to a situation where the identical beer receives a very low first rating. Moreover, herding effects tend to last a long time and are noticeable even after 20 or more ratings. Our results have important implications for the design of better rating systems.

Includes members:
  Member "rating effects on social news posts and comments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "characterizing behavioral trends in a community driven discussion platform")
  Member "learning attitudes and attributes from multi aspect reviews":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi dimensional explanation of ratings from reviews")

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)