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: "nepotistic relationships in twitter and their impact on rank prestige algorithms"
  Abstract: Abstract   Micro-blogging services such as Twitter allow anyone to publish anything, anytime. Needless to say, many of the available contents can be diminished as babble or spam. However, given the number and diversity of users, some valuable pieces of information should arise from the stream of tweets. Thus, such services can develop into valuable sources of up-to-date information (the so-called real-time web) provided a way to find the most relevant/trustworthy/authoritative users is available. Hence, this makes a highly pertinent question for which graph centrality methods can provide an answer. In this paper the author offers a comprehensive survey of feasible algorithms for ranking users in social networks, he examines their vulnerabilities to linking malpractice in such networks, and suggests an objective criterion against which to compare such algorithms. Additionally, he suggests a first step towards “desensitizing” prestige algorithms against cheating by spammers and other abusive users.

Connected through hyperedge HE-1 (induced by source paper "modeling user reputation in online social networks the role of costs benefits and reciprocity"):
  with members:
    - "social resilience in online communities the autopsy of friendster"

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"
    - "detecting real world influence through twitter"
    - "influencetracker rating the impact of a twitter account"
    - "de retibus socialibus et legibus momenti"
    - "influence and passivity in social media"

Connected through hyperedge HE-3 (induced by source paper "who watches and shares what on youtube and when using twitter to understand youtube viewership"):
  with members:
    - "social resilience in online communities the autopsy of friendster"
    - "partisan asymmetries in online political activity"
    - "dynamical classes of collective attention in twitter"
    - "trends in social media persistence and decay"
    - "predicting the popularity of online content"

Connected through hyperedge HE-4 (induced by source paper "lurking in social networks topology based analysis and ranking methods"):
  with members:
    - "analyzing user behavior across social sharing environments"

Connected through hyperedge HE-5 (induced by source paper "de retibus socialibus et legibus momenti"):
  with members:
    - "influence and passivity in social media"
    - "big macs and eigenfactor scores don t let correlation coefficients fool you"
    - "social networks that matter twitter under the microscope"

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.IR(Information Retrieval)