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: "social promoter score sps and review network a method and a tool for predicting financial health of an online shopping brand"
  Abstract: The conventional way of summarizing ratings or sentiment of reviews of customers on products of an online shopping brand are not sufficient to evaluate the financial health of that brand. It overlooks the social standing and influence of individual customers. In this paper, we have proposed a tool named as Review Network for measuring the influence of customers in online merchandise sites like Amazon.com. Using this measured influence, we have proposed a method that evaluates loyalty of customers of a brand based on their ratings and sentiments of their reviews collected from online merchandise sites. Review network of a brand is built from all the reviews of all the products from that brand where nodes are customers and an edge is created if a customer becomes a potential reader of a review written by another customer. The centrality of a customer in that review network represents her influence. Our proposed method named as Social Promoter Score combines loyalty and centrality of all customers of a brand. We have compared our method with a baseline approach based on the concept of Net Promoter Score . We have applied Social Promoter Score on Amazon.com review data set of some well-known brands. Results show that Social Promoter Score predicts financial health of a brand in terms of future sales much better than baseline method. We have noticed that in general effects of Social Promoter Score reflect on the product sales in one to five months.

Connected through hyperedge HE-1 (induced by source paper "explicit feedbacks meet with implicit feedbacks a combined approach for recommendation system"):
  with members:
    - "neural collaborative filtering"
    - "collaborative deep learning for recommender systems"
    - "content based book recommending using learning for text categorization"

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.SI(Social and Information Networks)