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 "non altering time scales for aggregation of dynamic networks into series of graphs"):
  Abstract: Many dynamic networks coming from real-world contexts are link streams, i.e. a finite collection of triplets $(u,v,t)$ where $u$ and $v$ are two nodes having a link between them at time $t$. A very large number of studies on these objects start by aggregating the data in disjoint time windows of length $\Delta$ in order to obtain a series of graphs on which are made all subsequent analyses. Here we are concerned with the impact of the chosen $\Delta$ on the obtained graph series. We address the fundamental question of knowing whether a series of graphs formed using a given $\Delta$ faithfully describes the original link stream. We answer the question by showing that such dynamic networks exhibit a threshold for $\Delta$, which we call the \emph{saturation scale}, beyond which the properties of propagation of the link stream are altered, while they are mostly preserved before. We design an automatic method to determine the saturation scale of any link stream, which we apply and validate on several real-world datasets.

Includes members:
  Member "handling oversampling in dynamic networks using link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nonuniform timeslicing of dynamic graphs based on visual complexity")
  Member "detecting change points in the large scale structure of evolving networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "change detection in dynamic attributed networks")
  Member "identifying roles in an ip network with temporal and structural density":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predicting interactions between individuals with structural and dynamical information")
  Member "influence of the dynamic social network timeframe type and size on the group evolution discovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "different approaches to community evolution prediction in blogosphere")
  Member "centrality metric for dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nonlinearity networks a 2020 vision")

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)