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: "measuring user influence on twitter"
  Abstract: We survey the several measures that exists in literature to rank influential users in Twitter network.We propose a classification of these measures according to different criteria, such as the kind of metrics, the use of PageRank algorithm, the use of content analysis, among others.Besides the influential users, we also study measures of activity and popularity.We finish by mentioning some aspects of this topic related with computational complexity and correlation measures. Centrality is one of the most studied concepts in social network analysis. There is a huge literature regarding centrality measures, as ways to identify the most relevant users in a social network. The challenge is to find measures that can be computed efficiently, and that can be able to classify the users according to relevance criteria as close as possible to reality. We address this problem in the context of the Twitter network, an online social networking service with millions of users and an impressive flow of messages that are published and spread daily by interactions between users. Twitter has different types of users, but the greatest utility lies in finding the most influential ones. The purpose of this article is to collect and classify the different Twitter influence measures that exist so far in literature. These measures are very diverse. Some are based on simple metrics provided by the Twitter API, while others are based on complex mathematical models. Several measures are based on the PageRank algorithm, traditionally used to rank the websites on the Internet. Some others consider the timeline of publication, others the content of the messages, some are focused on specific topics, and others try to make predictions. We consider all these aspects, and some additional ones. Furthermore, we include measures of activity and popularity, the traditional mechanisms to correlate measures, and some important aspects of computational complexity for this particular context.

Connected through hyperedge HE-1 (induced by source paper "political elections under social fire analysis and detection of propaganda on twitter"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "election campaigning on social media politicians audiences and the mediation of political communication on facebook and twitter"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "deep neural networks for bot detection"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"

Connected through hyperedge HE-2 (induced by source paper "a customisable pipeline for continuously harvesting socially minded twitter users"):
  with members:
    - "recruiting from the network discovering twitter users who can help combat zika epidemics"
    - "the perceived assortativity of social networks methodological problems and solutions"
    - "semantifying twitter the influencetracker ontology"
    - "demon a local first discovery method for overlapping communities"

Connected through hyperedge HE-3 (induced by source paper "a tensor based data model for polystore an application to social networks data"):
  with members:
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "polystore mathematics of relational algebra"
    - "the bigdawg polystore system and architecture"
    - "d4m bringing associative arrays to database engines"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "the sql query language configurable unifying and semi structured"
    - "predicting successful memes using network and community structure"
    - "exposing multi relational networks to single relational network analysis algorithms"

Connected through hyperedge HE-4 (induced by source paper "predicting rising follower counts on twitter using profile information"):
  with members:
    - "influencetracker rating the impact of a twitter account"
    - "the pulse of news in social media forecasting popularity"

Connected through hyperedge HE-5 (induced by source paper "finding influential users in social media using association rule learning"):
  with members:
    - "predicting community evolution in social networks"
    - "key user extraction based on telecommunication data aka key users in social network how to find them"
    - "the multidimensional study of viral campaigns as branching processes"
    - "predicting influential users in online 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.SI(Social and Information Networks)