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: "temporal opinion spam detection by multivariate indicative signals"
  Abstract: Online consumer reviews reflect the testimonials of real people, unlike advertisements. As such, they have critical impact on potential consumers, and indirectly on businesses. According to a Harvard study (Luca 2011), +1 rise in star-rating increases revenue by 5-9%. Problematically, such financial incentives have created a market for spammers to fabricate reviews, to unjustly promote or demote businesses, activities known as opinion spam (Jindal and Liu 2008). A vast majority of existing work on this problem have formulations based on static review data, with respective techniques operating in an offline fashion. Spam campaigns, however, are intended to make most impact during their course. Abnormal events triggered by spammers' activities could be masked in the load of future events, which static analysis would fail to identify. In this work, we approach the opinion spam problem with a temporal formulation. Specifically, we monitor a list of carefully selected indicative signals of opinion spam over time and design efficient techniques to both detect and characterize abnormal events in real-time. Experiments on datasets from two different review sites show that our approach is fast, effective, and practical to be deployed in real-world systems.

Connected through hyperedge HE-1 (induced by source paper "detection of review abuse via semi supervised binary multi target tensor decomposition"):
  with members:
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "in a world that counts clustering and detecting fake social engagement at scale"
    - "birdnest bayesian inference for ratings fraud detection"
    - "on the expressive power of deep learning a tensor analysis"
    - "new insights and perspectives on the natural gradient method"

Connected through hyperedge HE-2 (induced by source paper "false information on web and social media a survey"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "the spread of low credibility content by social bots"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "an army of me sockpuppets in online discussion communities"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "automatic detection of fake news"
    - "beyond news contents the role of social context for fake news detection"

Connected through hyperedge HE-3 (induced by source paper "securing behavior based opinion spam detection"):
  with members:
    - "practical attacks against graph based clustering"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "a general retraining framework for scalable adversarial classification"
    - "evasion attacks against machine learning at test time"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-4 (induced by source paper "e commerce anomaly detection a bayesian semi supervised tensor decomposition approach using natural gradients"):
  with members:
    - "in a world that counts clustering and detecting fake social engagement at scale"
    - "birdnest bayesian inference for ratings fraud detection"

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)