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: "a data based approach to social influence maximization"
  Abstract: Influence maximization is the problem of finding a set of users in a social network, such that by targeting this set, one maximizes the expected spread of influence in the network. Most of the literature on this topic has focused exclusively on the social graph, overlooking historical data, i.e., traces of past action propagations. In this paper, we study influence maximization from a novel data-based perspective. In particular, we introduce a new model, which we call credit distribution, that directly leverages available propagation traces to learn how influence flows in the network and uses this to estimate expected influence spread. Our approach also learns the different levels of influenceability of users, and it is time-aware in the sense that it takes the temporal nature of influence into account. We show that influence maximization under the credit distribution model is NP-hard and that the function that defines expected spread under our model is submodular. Based on these, we develop an approximation algorithm for solving the influence maximization problem that at once enjoys high accuracy compared to the standard approach, while being several orders of magnitude faster and more scalable.

Connected through hyperedge HE-1 (induced by source paper "use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making"):
  with members:
    - "leveraging motifs to model the temporal dynamics of diffusion networks"
    - "impact of social influence on adoption behavior an online controlled experimental evaluation"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "cumulative effect in information diffusion empirical study on a microblogging network"
    - "what stops social epidemics"
    - "co evolution of selection and influence in social networks"

Connected through hyperedge HE-2 (induced by source paper "computational aspects of optimal strategic network diffusion"):
  with members:
    - "adaptive influence maximization in dynamic social networks"
    - "balancing speed and coverage by sequential seeding in complex networks"
    - "scalable methods for adaptively seeding a social network"
    - "analytical approach to parallel repetition"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "evidential positive opinion influence measures for viral marketing"):
  with members:
    - "two evidential data based models for influence maximization in twitter"
    - "maximizing positive opinion influence using an evidential approach"
    - "median evidential c means algorithm and its application to community detection"
    - "classification of message spreading in a heterogeneous social network"
    - "dynamic time warping distance for message propagation classification in twitter"

Connected through hyperedge HE-4 (induced by source paper "efficient approximation algorithms for adaptive seed minimization"):
  with members:
    - "cost effective seed selection in online social networks"
    - "influence maximization meets efficiency and effectiveness a hop based approach"
    - "adaptive influence maximization in social networks why commit when you can adapt"
    - "holistic influence maximization combining scalability and efficiency with opinion aware models"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "scalable methods for adaptively seeding a social network"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "imrank influence maximization via finding self consistent ranking"

Connected through hyperedge HE-5 (induced by source paper "automatic ensemble learning for online influence maximization"):
  with members:
    - "thompson sampling for stochastic bandits with graph feedback"
    - "model independent online learning for influence maximization"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "online influence maximization"
    - "influence maximization with bandits"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "an information theoretic analysis of thompson sampling"

Connected through hyperedge HE-6 (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-7 (induced by source paper "social influence and radicalization a social data analytics study"):
  with members:
    - "adaptive influence maximization in dynamic social networks"
    - "data curation apis"
    - "least cost influence maximization across multiple social networks"
    - "failedrevolutions using twitter to study the antecedents of isis support"
    - "klout score measuring influence across multiple social networks"
    - "online social media in the syria conflict encompassing the extremes and the in betweens"
    - "big data and cross document coreference resolution current state and future opportunities"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-8 (induced by source paper "seed selection and social coupon allocation for redemption maximization in online social networks"):
  with members:
    - "stochastic coupon probing in social networks"
    - "influence maximization meets efficiency and effectiveness a hop based approach"
    - "towards profit maximization for online social network providers"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "when social influence meets item inference"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "profit maximization over social networks"
    - "irie scalable and robust influence maximization in social networks"

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.DB(Databases)