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: "truetop a sybil resilient system for user influence measurement on twitter"
  Abstract: Influential users have great potential for accelerating information dissemination and acquisition on Twitter. How to measure the influence of Twitter users has attracted significant academic and industrial attention. Existing influence measurement techniques are vulnerable to sybil users that are thriving on Twitter. Although sybil defenses for online social networks have been extensively investigated, they commonly assume unique mappings from human-established trust relationships to online social associations and thus do not apply to Twitter where users can freely follow each other. This paper presents TrueTop, the first sybil-resilient system to measure the influence of Twitter users. TrueTop is rooted in two observations from real Twitter datasets. First, although non-sybil users may incautiously follow strangers, they tend to be more careful and selective in retweeting, replying to, and mentioning other users. Second, influential users usually get much more retweets, replies, and mentions than non-influential users. Detailed theoretical studies and synthetic simulations show that TrueTop can generate very accurate influence measurement results with strong resilience to sybil attacks.

Connected through hyperedge HE-1 (induced by source paper "structure based sybil detection in social networks via local rule based propagation"):
  with members:
    - "sybilfuse combining local attributes with global structure to perform robust sybil detection"
    - "sybilbelief a semi supervised learning approach for structure based sybil detection"
    - "social turing tests crowdsourcing sybil detection"
    - "uncovering social network sybils in the wild"

Connected through hyperedge HE-2 (induced by source paper "privacy and security in online social networks a survey"):
  with members:
    - "social fingerprinting detection of spambot groups through dna inspired behavioral modeling"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "the rise of social bots"
    - "privacy concerns vs user behavior in community question answering"
    - "special issue on foundations of social computing enabling social applications via decentralized social data management"
    - "the social world of content abusers in community question answering"
    - "online social networks threats and solutions"
    - "sybilbelief a semi supervised learning approach for structure based sybil detection"

Connected through hyperedge HE-3 (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"
    - "klout score measuring influence across multiple social networks"
    - "detecting real world influence through twitter"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "the rise of social botnets attacks and countermeasures"):
  with members:
    - "the rise of social bots"
    - "manipulation and abuse on social media by emilio ferrara with ching man au yeung as coordinator"
    - "reverse engineering socialbot infiltration strategies in twitter"
    - "truthy mapping the spread of astroturf in microblog streams"

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)