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 "fairjudge trustworthy user prediction in rating platforms"):
  Abstract: Rating platforms enable large-scale collection of user opinion about items (products, other users, etc.). However, many untrustworthy users give fraudulent ratings for excessive monetary gains. In the paper, we present FairJudge, a system to identify such fraudulent users. We propose three metrics: (i) the fairness of a user that quantifies how trustworthy the user is in rating the products, (ii) the reliability of a rating that measures how reliable the rating is, and (iii) the goodness of a product that measures the quality of the product. Intuitively, a user is fair if it provides reliable ratings that are close to the goodness of the product. We formulate a mutually recursive definition of these metrics, and further address cold start problems and incorporate behavioral properties of users and products in the formulation. We propose an iterative algorithm, FairJudge, to predict the values of the three metrics. We prove that FairJudge is guaranteed to converge in a bounded number of iterations, with linear time complexity. By conducting five different experiments on five rating platforms, we show that FairJudge significantly outperforms nine existing algorithms in predicting fair and unfair users. We reported the 100 most unfair users in the Flipkart network to their review fraud investigators, and 80 users were correctly identified (80% accuracy). The FairJudge algorithm is already being deployed at Flipkart.

Includes members:
  Member "antisocial behavior in online discussion communities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "empirical analysis of multi task learning for reducing model bias in toxic comment detection")
  Member "birdnest bayesian inference for ratings fraud detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the future of misinformation detection new perspectives and trends")
  Member "detecting singleton review spammers using semantic similarity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "false information on web and social media a survey")
  Member "graph based anomaly detection and description a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "temporal probability calibration")
  Member "battling the internet water army detection of hidden paid posters":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards understanding the information ecosystem through the lens of multiple web communities")

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)