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: "fairplay fraud and malware detection in google play"
  Abstract: Fraudulent behaviors in Google Android app market fuel search rank abuse and malware proliferation. We present FairPlay, a novel system that uncovers both malware and search rank fraud apps, by picking out trails that fraudsters leave behind. To identify suspicious apps, FairPlay PCF algorithm correlates review activities and uniquely combines detected review relations with linguistic and behavioral signals gleaned from longitudinal Google Play app data. We contribute a new longitudinal app dataset to the community, which consists of over 87K apps, 2.9M reviews, and 2.4M reviewers, collected over half a year. FairPlay achieves over 95% accuracy in classifying gold standard datasets of malware, fraudulent and legitimate apps. We show that 75% of the identified malware apps engage in search rank fraud. FairPlay discovers hundreds of fraudulent apps that currently evade Google Bouncer detection technology, and reveals a new type of attack campaign, where users are harassed into writing positive reviews, and install and review other apps.

Connected through hyperedge HE-1 (induced by source paper "uncovering download fraud activities in mobile app markets"):
  with members:
    - "beyond google play a large scale comparative study of chinese android app markets"
    - "smoke screener or straight shooter detecting elite sybil attacks in user review social networks"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "xgboost a scalable tree boosting system"
    - "the dark side of micro task marketplaces characterizing fiverr and automatically detecting crowdturfing"
    - "paying for likes understanding facebook like fraud using honeypots"
    - "serf and turf crowdturfing for fun and profit"

Connected through hyperedge HE-2 (induced by source paper "search rank fraud de anonymization in online systems"):
  with members:
    - "birdnest bayesian inference for ratings fraud detection"
    - "deepwalk online learning of social representations"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-3 (induced by source paper "stateless puzzles for real time online fraud preemption"):
  with members:
    - "graph based anomaly detection and description a survey"

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)