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: "on the interplay between social and topical structure"
  Abstract: People's interests and people's social relationships are intuitively connected, but understanding their interplay and whether they can help predict each other has remained an open question. We examine the interface of two decisive structures forming the backbone of online social media: the graph structure of social networks - who connects with whom - and the set structure of topical affiliations - who is interested in what. In studying this interface, we identify key relationships whereby each of these structures can be understood in terms of the other. The context for our analysis is Twitter, a complex social network of both follower relationships and communication relationships. On Twitter, "hashtags" are used to label conversation topics, and we examine hashtag usage alongside these social structures. #R##N#We find that the hashtags that users adopt can predict their social relationships, and also that the social relationships between the initial adopters of a hashtag can predict the future popularity of that hashtag. By studying weighted social relationships, we observe that while strong reciprocated ties are the easiest to predict from hashtag structure, they are also much less useful than weak directed ties for predicting hashtag popularity. Importantly, we show that computationally simple structural determinants can provide remarkable performance in both tasks. While our analyses focus on Twitter, we view our findings as broadly applicable to topical affiliations and social relationships in a host of diverse contexts, including the movies people watch, the brands people like, or the locations people frequent.

Connected through hyperedge HE-1 (induced by source paper "modeling information diffusion in online social networks with partial differential equations"):
  with members:
    - "information diffusion and external influence in networks"
    - "diffusive logistic model towards predicting information diffusion in online social networks"
    - "what stops social epidemics"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "a framework for quantitative analysis of cascades on networks"

Connected through hyperedge HE-2 (induced by source paper "are all successful communities alike characterizing and predicting the success of online communities"):
  with members:
    - "network structure efficiency and performance in wikiprojects"
    - "tracing community genealogy how new communities emerge from the old"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "a warm welcome matters the link between social feedback and weight loss in r loseit"
    - "loyalty in online communities"
    - "social networks under stress"
    - "the effect of social feedback in a reddit weight loss community"
    - "all who wander on the prevalence and characteristics of multi community engagement"

Connected through hyperedge HE-3 (induced by source paper "an experimental study of structural diversity in social networks"):
  with members:
    - "the lifecycles of apps in a social ecosystem"
    - "popularity prediction in microblogging network a case study on sina weibo"
    - "characterizing and curating conversation threads expansion focus volume re entry"
    - "virality prediction and community structure in social networks"
    - "social influence in social advertising evidence from field experiments"

Connected through hyperedge HE-4 (induced by source paper "tracing community genealogy how new communities emerge from the old"):
  with members:
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "community identity and user engagement in a multi community landscape"
    - "loyalty in online communities"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "averaging gone wrong using time aware analyses to better understand behavior"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "can cascades be predicted"
    - "evolution of reddit from the front page of the internet to a self referential community"

Connected through hyperedge HE-5 (induced by source paper "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"):
  with members:
    - "time matters multi scale temporalization of social media popularity"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "item popularity prediction in e commerce using image quality feature vectors"
    - "beyond exchangeability the chinese voting process"
    - "learning distributed representations of sentences from unlabelled data"
    - "understanding image virality"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "competition and selection among conventions"):
  with members:
    - "the status gradient of trends in social media"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "cascades a view from audience"):
  with members:
    - "bieber blast bieberblast early prediction of popular hashtag compounds"
    - "can cascades be predicted"
    - "bigbirds never die understanding social dynamics of emergent hashtag"
    - "you had me at hello how phrasing affects memorability"

Connected through hyperedge HE-8 (induced by source paper "cascading behavior in yelp reviews"):
  with members:
    - "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)