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: "in a world that counts clustering and detecting fake social engagement at scale"
  Abstract: How can web services that depend on user generated content discern fake social engagement activities by spammers from legitimate ones? In this paper, we focus on the social site of YouTube and the problem of identifying bad actors posting inorganic contents and inflating the count of social engagement metrics. We propose an effective method, Leas (Local Expansion at Scale), and show how the fake engagement activities on YouTube can be tracked over time by analyzing the temporal graph based on the engagement behavior pattern between users and YouTube videos. With the domain knowledge of spammer seeds, we formulate and tackle the problem in a semi-supervised manner --- with the objective of searching for individuals that have similar pattern of behavior as the known seeds --- based on a graph diffusion process via local spectral subspace. We offer a fast, scalable MapReduce deployment adapted from the localized spectral clustering algorithm. We demonstrate the effectiveness of our deployment at Google by achieving a manual review accuracy of 98% on YouTube Comments graph in practice. Comparing with the state-of-the-art algorithm CopyCatch, Leas achieves 10 times faster running time on average. Leas is now actively in use at Google, searching for daily deceptive practices on YouTube's engagement graph spanning over a billion users.

Connected through hyperedge HE-1 (induced by source paper "ties temporal interaction embeddings for enhancing social media integrity at facebook"):
  with members:
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "pytorch biggraph a large scale graph embedding system"
    - "poised spotting twitter spam off the beaten paths"
    - "deep learning for hate speech detection in tweets"
    - "attention is all you need"
    - "a critical review of recurrent neural networks for sequence learning"

Connected through hyperedge HE-2 (induced by source paper "how impersonators exploit instagram to generate fake engagement"):
  with members:
    - "netspam a network based spam detection framework for reviews in online social media"
    - "the rise of social bots"
    - "mytweet via instagram exploring user behaviour across multiple social networks"
    - "on the reliability of profile matching across large online social networks"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"

Connected through hyperedge HE-3 (induced by source paper "a survey on big data for network traffic monitoring and analysis"):
  with members:
    - "data analytics service composition and deployment on edge devices"
    - "net2vec deep learning for the network"
    - "network volume anomaly detection and identification in large scale networks based on online time structured traffic tensor tracking"
    - "a new approach to dimensionality reduction for anomaly detection in data traffic"
    - "efficacy of live ddos detection with hadoop"
    - "the family of mapreduce and large scale data processing systems"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-4 (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"
    - "temporal opinion spam detection by multivariate indicative signals"
    - "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-5 (induced by source paper "instagram fake and automated account detection"):
  with members:
    - "fame for sale"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-6 (induced by source paper "e commerce anomaly detection a bayesian semi supervised tensor decomposition approach using natural gradients"):
  with members:
    - "temporal opinion spam detection by multivariate indicative signals"
    - "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)