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 "homophily and triadic closure in evolving social networks"):
  Abstract: We present a new network model accounting for homophily and triadic closure in the evolution of social networks. In particular, in our model, each node is characterized by a number of features and the probability of a link between two nodes depends on common features. The bipartite network of the actors and features evolves according to a dynamics that depends on three parame-ters that respectively regulate the preferential attachment in the transmission of the features to the nodes, the number of new features per node, and the power-law behavior of the total number of observed features. We provide theoretical results and statistical estimators for the parameters of the model. We validate our approach by means of simulations and an empirical analysis of a network of scientifc collaborations.

Includes members:
  Member "science vs conspiracy collective narratives in the age of misinformation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the covid 19 social media infodemic")
  Member "collective attention in the age of mis information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "computational human dynamics")
  Member "a network model characterized by a latent attribute structure with competition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimating latent feature feature interactions in large feature rich graphs")
  Member "social determinants of content selection in the age of mis information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vocabulary based method for quantifying controversy in social media")
  Member "nonparametric link prediction in dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dyngraph2vec capturing network dynamics using dynamic graph representation 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)