human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "evolution of social attribute networks measurements modeling and implications using google"):
  Abstract: Understanding social network structure and evolution has important implications for many aspects of network and system design including provisioning, bootstrapping trust and reputation systems via social networks, and defenses against Sybil attacks. Several recent results suggest that augmenting the social network structure with user attributes (e.g., location, employer, communities of interest) can provide a more fine-grained understanding of social networks. However, there have been few studies to provide a systematic understanding of these effects at scale. We bridge this gap using a unique dataset collected as the Google+ social network grew over time since its release in late June 2011. We observe novel phenomena with respect to both standard social network metrics and new attribute-related metrics (that we define). We also observe interesting evolutionary patterns as Google+ went from a bootstrap phase to a steady invitation-only stage before a public release. Based on our empirical observations, we develop a new generative model to jointly reproduce the social structure and the node attributes. Using theoretical analysis and empirical evaluations, we show that our model can accurately reproduce the social and attribute structure of real social networks. We also demonstrate that our model provides more accurate predictions for practical application contexts.

Includes members:
  Member "google or google examining the popularity of the new osn":
    also connected through other hyperedges:
      (none)
  Member "multi scale dynamics in a massive online social network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the fundamental equation of user dynamics and the structure of online social networks")
  Member "constructing and sampling graphs with a prescribed joint degree distribution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "common greedy wiring and rewiring heuristics do not guarantee maximum assortative graphs of given degree")
  Member "predicting links and inferring attributes using a social attribute network san":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "clustering attributed graphs models measures and methods")
  Member "four degrees of separation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "diffusion improves graph learning")
  Member "multiplicative attribute graph model of real world networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph generators state of the art and open challenges")
  Member "hyperanf approximating the neighbourhood function of very large graphs on a budget":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")
  Member "systematic topology analysis and generation using degree correlations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "recent advances in scalable network generation")

Question: Please tell me which class the source paper 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)