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: "understanding and overcoming biases in customer reviews"
  Abstract: Our paper contributes to the literature recommending approaches to make online reviews more credible and representative. We analyze data from four diverse major online retailers and find that verified customers who are prompted (by an email) to write a review, submit, on average, up to 0.5 star higher ratings than self-motivated web reviewers. Moreover, these email-prompted reviews remain stable over time, whereas web reviews exhibit a downward trend. This finding provides support for the existence of social influence and selection biases during the submission of a web review, when social signals are being displayed. In contrast, no information about the current state of the reviews is displayed in the email promptings. Moreover, we find that when a retailer decides to start sending email promptings, the existing population of web reviewers is unaffected both in their volume as well as the characteristics of their submitted reviews. We explore how our combined findings can suggest ways to mitigate various biases that govern online review submissions and help practitioners provide more credible, representative and higher ratings to their customers.

Connected through hyperedge HE-1 (induced by source paper "you write like you eat stylistic variation as a predictor of social stratification"):
  with members:
    - "bleaching text abstract features for cross lingual gender prediction"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "distant supervision for emotion detection using facebook reactions"
    - "learning when to trust distant supervision an application to low resource pos tagging using cross lingual projection"
    - "computational sociolinguistics a survey"
    - "adam a method for stochastic optimization"
    - "the readability of tweets and their geographic correlation with education"

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.HC(Human-Computer Interaction)