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: "the status gradient of trends in social media"
  Abstract: An active line of research has studied the detection and representation of trends in social media content. There is still relatively little understanding, however, of methods to characterize the early adopters of these trends: who picks up on these trends at different points in time, and what is their role in the system? We develop a framework for analyzing the population of users who participate in trending topics over the course of these topics' lifecycles. Central to our analysis is the notion of a "status gradient", describing how users of different activity levels adopt a trend at different points in time. Across multiple datasets, we find that this methodology reveals key differences in the nature of the early adopters in different domains.

Connected through hyperedge HE-1 (induced by source paper "the road to success assessing the fate of linguistic innovations in online communities"):
  with members:
    - "competition and selection among conventions"
    - "tracing the use of practices through networks of collaboration"
    - "loyalty in online communities"
    - "the social dynamics of language change in online networks"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "diffusion of lexical change in social media"
    - "niche as a determinant of word fate in online groups"
    - "weak ties subtle role of information diffusion in online social networks"

Connected through hyperedge HE-2 (induced by source paper "competition and selection among conventions"):
  with members:
    - "bieber blast bieberblast early prediction of popular hashtag compounds"
    - "the social dynamics of language change in online networks"
    - "the effects of age gender and region on non standard linguistic variation in online social networks"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "can cascades be predicted"
    - "multi level modeling of quotation families morphogenesis"
    - "you had me at hello how phrasing affects memorability"
    - "on the interplay between social and topical structure"

Connected through hyperedge HE-3 (induced by source paper "tracing the use of practices through networks of collaboration"):
  with members:
    - "competition and selection among conventions"
    - "can cascades be predicted"

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)