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: "estimating the prevalence of deception in online review communities"
  Abstract: Consumers' purchase decisions are increasingly influenced by user-generated online reviews. Accordingly, there has been growing concern about the potential for posting "deceptive opinion spam" -- fictitious reviews that have been deliberately written to sound authentic, to deceive the reader. But while this practice has received considerable public attention and concern, relatively little is known about the actual prevalence, or rate, of deception in online review communities, and less still about the factors that influence it. #R##N#We propose a generative model of deception which, in conjunction with a deception classifier, we use to explore the prevalence of deception in six popular online review communities: Expedia, Hotels.com, Orbitz, Priceline, TripAdvisor, and Yelp. We additionally propose a theoretical model of online reviews based on economic signaling theory, in which consumer reviews diminish the inherent information asymmetry between consumers and producers, by acting as a signal to a product's true, unknown quality. We find that deceptive opinion spam is a growing problem overall, but with different growth rates across communities. These rates, we argue, are driven by the different signaling costs associated with deception for each review community, e.g., posting requirements. When measures are taken to increase signaling cost, e.g., filtering reviews written by first-time reviewers, deception prevalence is effectively reduced.

Connected through hyperedge HE-1 (induced by source paper "why is chicago deceptive towards building model driven tutorials for humans"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "a human grounded evaluation of shap for alert processing"
    - "rating reliability and bias in news articles does ai assistance help everyone"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "efficient search for diverse coherent explanations"
    - "local rule based explanations of black box decision systems"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"

Connected through hyperedge HE-2 (induced by source paper "on human predictions with explanations and predictions of machine learning models a case study on deception detection"):
  with members:
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "do explanations make vqa models more predictable to a human"
    - "teaching categories to human learners with visual explanations"
    - "a unified approach to interpreting model predictions"
    - "algorithmic decision making and the cost of fairness"
    - "improving human machine cooperative visual search with soft highlighting"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"

Connected through hyperedge HE-3 (induced by source paper "teevil identity lease via trusted execution environments"):
  with members:
    - "stay on topic generating context specific fake restaurant reviews"
    - "fake news detection on social media a data mining perspective"
    - "software grand exposure sgx cache attacks are practical"
    - "rollback and forking detection for trusted execution environments using lightweight collective memory"
    - "automated crowdturfing attacks and defenses in online review systems"
    - "inferring fine grained control flow inside sgx enclaves with branch shadowing"
    - "debunking in a world of tribes"
    - "can cascades be predicted"

Connected through hyperedge HE-4 (induced by source paper "online deception detection refueled by real world data collection"):
  with members:
    - "convolutional neural networks for sentence classification"
    - "recurrent neural network regularization"
    - "sequence to sequence learning with neural networks"
    - "distributed representations of words and phrases and their compositionality"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-5 (induced by source paper "netspam a network based spam detection framework for reviews in online social media"):
  with members:
    - "spreading processes in multilayer networks"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-6 (induced by source paper "graph based anomaly detection and description a survey"):
  with members:
    - "detecting change points in the large scale structure of evolving networks"
    - "deltacon a principled massive graph similarity function"
    - "discriminative probabilistic models for relational data"
    - "role dynamics fast mining of large dynamic networks"
    - "identifying the signs of fraudulent accounts using data mining techniques"
    - "netsimile a scalable approach to size independent network similarity"
    - "measuring tie strength in implicit social networks"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-7 (induced by source paper "voting for deceptive opinion spam detection"):
  with members:
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-8 (induced by source paper "how community feedback shapes user behavior"):
  with members:
    - "how opinions are received by online communities a case study on amazon com helpfulness votes"

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)