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 data analysis a study on friend rating influence"):
  Abstract: Social Networking accounts for a significant chunk of interest among various online activities~\cite{smith2009social}. The proclivity of being social, online, has been ingrained in us so much that we are actively producing content for the rest of the world to see or take interest in our whereabouts, our meals, our opinions, photographs etc. Yelp (this https URL), seamlessly, integrates this very aspect of people in its portal. It engages people to write reviews about the businesses they have availed the services of, rate them, add photographs, tags, follow other people and their activities, etc. In this paper we examine and present the co-relation between a user's rating and the influence of the people, that the user follows, on the user for a particular business. The group of people that the user follows is commonly referred as friends of the user. We also analyze if a user can get influenced, if a business has a certain number of reviews already present or if the reviews have been written by elite reviewers (a reviewer who, according to Yelp, has contributed exceptionally in engaging the community in the form of consistency in writing reviews, as well as the quality of the reviews). Our analysis, through correlation and regression techniques, is able to prove that the user's rating remains unaffected by the number of people a user was friends with nor does the existing number of reviews and presence of elite reviewers helps in influencing a user. What shapes a user's rating is the overall experience, that the user had at the restaurant.

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 "analysis of yelp reviews":
    also connected through other hyperedges:
      (none)

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.SI(Social and Information Networks)