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 "rating the online review rating system using yelp"):
  Abstract: The impact of ratings on a restaurant plays a major role in attracting future customers to that restaurant. The word of mouth has been systematically replaced with the online reviews. It gives a sense of satisfaction for people to know beforehand about the number of average stars the restaurant has acquired before stepping into a restaurant. However, these ratings are indirectly biased based on the location, amenities, and the perception of individual people. In this work, we analyze the ratings of restaurants available through the Yelp public data for the discrepancies in the rating system and attempt to provide an optimized global rating system. For a frequent visitor to a high- end restaurant with lavish amenities, even a slightest of reduction in the expected ambiance may prompt a 4 star rating, while a restaurant, which guarantees a minimum taste for its food, may get a 5 star rating. These discrepancies can often be attributed to three factors- the perspective of individual people, features of the restaurant and Location. The perspective of individual people is always subjective and what seems good for one person may be poor for another. In this work, we focus on the other two important factors, Reviews and the features.

Includes members:
  Member "predicting a business star in yelp from its reviews text alone":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leveraging aspect phrase embeddings for cross domain review rating prediction")
  Member "the warm start bias of yelp ratings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predicting a business star in yelp from its reviews text alone")
  Member "a new anew evaluation of a word list for sentiment analysis in microblogs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how impersonators exploit instagram to generate fake engagement")

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)