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 "dynamic clustering in social networks using louvain and infomap method"):
  Abstract: Finding communities or clusters in social networks is a fa- mous topic in social network analysis. Most algorithms are limited to static snapshots, so they cannot handle dynamics within the underlying graph. In this paper we present a modification of the Louvain community detec- tion method to handle changes in the graph without rerunning the full algorithm. Also, we adapted the Louvain greedy approach to optimize the Infomap measure. The main idea is, to recalculate only a small area around the changes. Depending on the graph size and the amount of changes, this yields a massive runtime decrease. As validation data, we provide a graph generator, which produces spe- cific community structures, at given times and also intermediate steps to transform the graph from one to another specific graph. Experiments show that runtime decrease is possible without much loss of quality. These values depend on the reprocessed area inside the graph.

Includes members:
  Member "predicting community evolution in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "community discovery in dynamic networks a survey")
  Member "multiscale network generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "centralities for networks with consumable resources")
  Member "community structure in large networks natural cluster sizes and the absence of large well defined clusters":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")
  Member "rhythms of social interaction messaging within a massive online network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "designing for health chatbots")

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)