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: "detecting real world influence through twitter"
  Abstract: In this paper, we investigate the issue of detecting the real-life influence of people based on their Twitter account. We propose an overview of common Twitter features used to characterize such accounts and their activity, and show that these are inefficient in this context. In particular, retweets and followers numbers, and Klout score are not relevant to our analysis. We thus propose several Machine Learning approaches based on Natural Language Processing and Social Network Analysis to label Twitter users as Influencers or not. We also rank them according to a predicted influence level. Our proposals are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art ranking methods.

Connected through hyperedge HE-1 (induced by source paper "a review of features for the discrimination of twitter users application to the prediction of offline influence"):
  with members:
    - "temporal multinomial mixture for instance oriented evolutionary clustering"
    - "klout score measuring influence across multiple social networks"
    - "who will retweet this automatically identifying and engaging strangers on twitter to spread information"
    - "identifying the community roles of social capitalists in the twitter network"
    - "understanding types of users on twitter"
    - "artex is another text summarizer"
    - "i wanted to predict elections with twitter and all i got was this lousy paper a balanced survey on election prediction using twitter data"
    - "comparative evaluation of community detection algorithms a topological approach"

Connected through hyperedge HE-2 (induced by source paper "measuring user influence on twitter"):
  with members:
    - "a review of features for the discrimination of twitter users application to the prediction of offline influence"
    - "truetop a sybil resilient system for user influence measurement on twitter"
    - "klout score measuring influence across multiple social networks"
    - "influencetracker rating the impact of a twitter account"
    - "nepotistic relationships in twitter and their impact on rank prestige algorithms"
    - "de retibus socialibus et legibus momenti"
    - "influence and passivity in social media"

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)