human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "do recommender systems benefit users a modeling approach"
  Abstract: Recommender systems are present in many web applications to guide purchase choices. They increase sales and benefit sellers, but whether they benefit customers by providing relevant products remains less explored. While in many cases the recommended products are relevant to users, in other cases customers may be tempted to purchase the products only because they are recommended. Here we introduce a model to examine the benefit of recommender systems for users, and find that recommendations from the system can be equivalent to random draws if one always follows the recommendations and seldom purchases according to his or her own preference. Nevertheless, with sufficient information about user preferences, recommendations become accurate and an abrupt transition to this accurate regime is observed for some of the studied algorithms. On the other hand, we find that high estimated accuracy indicated by common accuracy metrics is not necessarily equivalent to high real accuracy in matching users with products. This disagreement between estimated and real accuracy serves as an alarm for operators and researchers who evaluate recommender systems merely with accuracy metrics. We tested our model with a real dataset and observed similar behaviors. Finally, a recommendation approach with improved accuracy is suggested. These results imply that recommender systems can benefit users, but the more frequently a user purchases the recommended products, the less relevant the recommended products are in matching user taste.

Connected through hyperedge HE-1 (induced by source paper "the essential role of time in network based recommendation"):
  with members:
    - "information filtering by similarity preferential diffusion processes"
    - "trend prediction in temporal bipartite networks the case of movielens netflix and digg"
    - "social contagion an empirical study of information spread on digg and twitter follower graphs"

Question: Please tell me which class the center node 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)