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: "a framework for quantitative analysis of cascades on networks"
  Abstract: How does information flow in online social networks? How does the structure and size of the information cascade evolve in time? How can we efficiently mine the information contained in cascade dynamics? We approach these questions empirically and present an efficient and scalable mathematical framework for quantitative analysis of cascades on networks. We define a cascade generating function that captures the details of the microscopic dynamics of the cascades. We show that this function can also be used to compute the macroscopic properties of cascades, such as their size, spread, diameter, number of paths, and average path length. We present an algorithm to efficiently compute cascade generating function and demonstrate that while significantly compressing information within a cascade, it nevertheless allows us to accurately reconstruct its structure. We use this framework to study information dynamics on the social network of Digg. Digg allows users to post and vote on stories, and easily see the stories that friends have voted on. As a story spreads on Digg through voting, it generates cascades. We extract cascades of more than 3,500 Digg stories and calculate their macroscopic and microscopic properties. We identify several trends in cascade dynamics: spreading via chaining, branching and community. We discuss how these affect the spread of the story through the Digg social network. Our computational framework is general and offers a practical solution to quantitative analysis of the microscopic structure of even very large cascades.

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"
    - "on the interplay between social and topical structure"
    - "what stops social epidemics"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-2 (induced by source paper "computational human dynamics"):
  with members:
    - "joint embedding of structure and features via graph convolutional networks"
    - "location occupation and semantics based socioeconomic status inference on twitter"
    - "the temporal event graph"
    - "link transmission centrality in large scale social networks"
    - "socioeconomic dependencies of linguistic patterns in twitter a multivariate analysis"
    - "graph embedding techniques applications and performance a survey"
    - "correlations and dynamics of consumption patterns in social economic networks"
    - "stream graphs and link streams for the modeling of interactions over time"

Connected through hyperedge HE-3 (induced by source paper "sampling and reconstruction using bloom filters"):
  with members:
    - "bloofi multidimensional bloom filters"
    - "bloom filters in adversarial environments"
    - "can cascades be predicted"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"

Connected through hyperedge HE-4 (induced by source paper "modelling of information diffusion on social networks with applications to wechat"):
  with members:
    - "on diffusion restricted social network a measurement study of wechat moments"
    - "do cascades recur"
    - "cumulative effect in information diffusion empirical study on a microblogging network"
    - "the role of social networks in information diffusion"
    - "novelty and collective attention"

Connected through hyperedge HE-5 (induced by source paper "subgraph isomorphism in temporal networks"):
  with members:
    - "efficient subgraph matching on billion node graphs"
    - "capturing topology in graph pattern matching"

Connected through hyperedge HE-6 (induced by source paper "on the role of conductance geography and topology in predicting hashtag virality"):
  with members:
    - "can cascades be predicted"
    - "a bayesian approach for predicting the popularity of tweets"
    - "predicting successful memes using network and community structure"
    - "exploring image virality in google plus"
    - "virality prediction and community structure in social networks"
    - "exploring text virality in social networks"
    - "topic diffusion and emergence of virality in social networks"
    - "information diffusion and external influence in networks"

Connected through hyperedge HE-7 (induced by source paper "social contagion an empirical study of information spread on digg and twitter follower graphs"):
  with members:
    - "social dynamics of digg"
    - "what stops social epidemics"
    - "entropy based classification of retweeting activity on twitter"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "using a model of social dynamics to predict popularity of news"
    - "predicting influential users in online social networks"
    - "stochastic models of user contributory web sites"
    - "analysis of social voting patterns on digg"

Connected through hyperedge HE-8 (induced by source paper "rethinking centrality the role of dynamical processes in social network analysis"):
  with members:
    - "the simple rules of social contagion"
    - "social dynamics of digg"
    - "what stops social epidemics"
    - "entropy based classification of retweeting activity on twitter"
    - "parameterized centrality metric for network analysis"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "influence and passivity in social media"
    - "using stochastic models to describe and predict social dynamics of web users"

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)