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 "foundations of temporal text networks"):
  Abstract: Three fundamental elements to understand human information networks are the individuals in the network, the messages they exchange, that are often available online as text content (emails, social media posts, etc.), and the time when these interactions happen. An extremely large amount of research has addressed some of these aspects either in isolation or as combinations of two of them. There are also more and more works studying systems where all three elements are present, but typically using ad hoc models and algorithms that cannot be easily transfered to other contexts. Therefore, in this article we present a simple, expressive and extensible model for temporal text networks, that we claim can be used as a common ground across different types of networks and analysis tasks, and we show how simple procedures to produce views of the model allow the direct application of analysis methods already developed in other domains, from traditional data mining to multilayer network mining.

Includes members:
  Member "detecting socio economic impact of cultural investment through geo social network analysis":
    also connected through other hyperedges:
      (none)
  Member "graph embedding techniques applications and performance a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "motifs in temporal networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "automated analysis of topic actor networks on twitter new approach to the analysis of socio semantic networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "does the public discuss other topics on climate change than researchers a comparison of explorative networks based on author keywords and hashtags")
  Member "when is a network a network multi order graphical model selection in pathways and temporal networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "honem network embedding using higher order patterns in sequential data")
  Member "modelling sequences and temporal networks with dynamic community structures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computational human dynamics")
  Member "do cascades recur":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "estimating attention flow in online video networks")
  Member "clustering attributed graphs models measures and methods":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "community detection in node attributed social networks a survey")

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)