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 "changing the tune mixtures of network models that vary in time"):
  Abstract: Many of the complex systems we study in their representation as networks are growing objects, evolving by the addition of nodes and links over time. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closure. We demonstrate a method for estimating the relative roles of these mechanisms, and further, investigating how they change as the network evolves. We show that a rich class of network evolution models can be built from a weighted mixture of these model mechanisms. Using a likelihood based formulation we show how to calculate the optimal mixture for a given set of observations of network data, and show that this framework can be used to distinguish competing models that are indistinguishable by their summary statistics. Using real data from Facebook user interactions, we show that we can improve the ability of a model to reproduce network statistics using tuned model mixtures. We further investigate the idea that the underlying model of a network can change in time, for example, a technology based network might respond to changes in the underlying technology or a financial network might respond to economic shocks. Using artificial data we show that we can recapture the time at which a known change occurred. We use the Enron email dataset to show that we can estimate how mixtures of models change over time.

Includes members:
  Member "multiscale mixing patterns in networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "network constraints on the mixing patterns of binary node metadata")
  Member "choosing to grow a graph modeling network formation as discrete choice":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "choice set optimization under discrete choice models of group decisions")
  Member "motifs in temporal networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "detecting change points in the large scale structure of evolving networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "change detection in dynamic attributed networks")

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)