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 "evolving twitter an experimental analysis of graph properties of the social graph"):
  Abstract: Twitter is one of the most prominent Online Social Networks. It covers a significant part of the online worldwide population~20% and has impressive growth rates. The social graph of Twitter has been the subject of numerous studies since it can reveal the intrinsic properties of large and complex online communities. Despite the plethora of these studies, there is a limited cover on the properties of the social graph while they evolve over time. Moreover, due to the extreme size of this social network (millions of nodes, billions of edges), there is a small subset of possible graph properties that can be efficiently measured in a reasonable timescale. In this paper we propose a sampling framework that allows the estimation of graph properties on large social networks. We apply this framework to a subset of Twitter's social network that has 13.2 million users, 8.3 billion edges and covers the complete Twitter timeline (from April 2006 to January 2015). We derive estimation on the time evolution of 24 graph properties many of which have never been measured on large social networks. We further discuss how these estimations shed more light on the inner structure and growth dynamics of Twitter's social network.

Includes members:
  Member "aggregate characterization of user behavior in twitter and analysis of the retweet graph":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "vocabulary based method for quantifying controversy in social media")
  Member "two types of well followed users in the followership networks of twitter":
    also connected through other hyperedges:
      (none)
  Member "rethinking centrality the role of dynamical processes in social network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "centralities for networks with consumable resources")
  Member "four degrees of separation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "diffusion improves graph learning")

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)