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: "multi scale dynamics in a massive online social network"
  Abstract: Data confidentiality policies at major social network providers have severely limited researchers' access to large-scale datasets. The biggest impact has been on the study of network dynamics, where researchers have studied citation graphs and content-sharing networks, but few have analyzed detailed dynamics in the massive social networks that dominate the web today. In this paper, we present results of analyzing detailed dynamics in the Renren social network, covering a period of 2 years when the network grew from 1 user to 19 million users and 199 million edges. Rather than validate a single model of network dynamics, we analyze dynamics at different granularities (user-, community- and network- wide) to determine how much, if any, users are influenced by dynamics processes at different scales. We observe in- dependent predictable processes at each level, and find that while the growth of communities has moderate and sustained impact on users, significant events such as network merge events have a strong but short-lived impact that is quickly dominated by the continuous arrival of new users.

Connected through hyperedge HE-1 (induced by source paper "on the fundamental equation of user dynamics and the structure of online social networks"):
  with members:
    - "epidemiological modeling of online social network dynamics"

Connected through hyperedge HE-2 (induced by source paper "understanding service integration of online social networks a data driven study"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "snap a general purpose network analysis and graph mining library"
    - "studying social networks at scale macroscopic anatomy of the twitter social graph"
    - "social bootstrapping how pinterest and last fm social communities benefit by borrowing links from facebook"
    - "google or google dissecting the evolution of the new osn in its first year"
    - "the anatomy of the facebook social graph"

Connected through hyperedge HE-3 (induced by source paper "the many faces of graph dynamics"):
  with members:
    - "time centrality in dynamic complex networks"
    - "modeling and measuring graph similarity the case for centrality distance"
    - "deltacon a principled massive graph similarity function"
    - "centrality metric for dynamic networks"

Connected through hyperedge HE-4 (induced by source paper "reciprocal versus parasocial relationships in online social networks"):
  with members:
    - "dynamics of trust reciprocation in heterogenous mmog networks"
    - "directed closure measures for networks with reciprocity"
    - "a scalable null model for directed graphs matching all degree distributions in out and reciprocal"
    - "nonparametric link prediction in dynamic networks"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "predicting links and inferring attributes using a social attribute network san"
    - "four degrees of separation"

Connected through hyperedge HE-5 (induced by source paper "a place focused model for social networks in cities"):
  with members:
    - "brief encounters sensing modeling and visualizing urban mobility and copresence networks"

Connected through hyperedge HE-6 (induced by source paper "evolution of social attribute networks measurements modeling and implications using google"):
  with members:
    - "google or google examining the popularity of the new osn"
    - "constructing and sampling graphs with a prescribed joint degree distribution"
    - "predicting links and inferring attributes using a social attribute network san"
    - "four degrees of separation"
    - "multiplicative attribute graph model of real world networks"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"
    - "systematic topology analysis and generation using degree correlations"

Connected through hyperedge HE-7 (induced by source paper "google or google dissecting the evolution of the new osn in its first year"):
  with members:
    - "on the bursty evolution of online social networks"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "four degrees of separation"

Connected through hyperedge HE-8 (induced by source paper "google or google examining the popularity of the new osn"):
  with members:
    - "on the bursty evolution of online social networks"
    - "the anatomy of the facebook social graph"

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)