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 "modularity based dynamic community detection"):
  Abstract: In this project, we designed a modularity-based dynamic community detection method, which keeps tracking the community structure of a dynamic network with very low computing complexity in updating the community structure while the network changing dynamically, and we implemented our algorithm using Java. Furthermore, we generated random dynamic networks that have the community structures with different parameters as our experiment dataset. Extensive experiments have been taken to evaluate our method in terms of accuracy and efficiency. The experiment result shows that, comparing with Louvain algorithm, our method could greatly enhance the efficiency without jeopardizing much of the accuracy. Although we got a pretty good results from our experiments, we only implemented our method on our own generated dataset. For future work, we might want to test our method on real public dataset. In the real public dynamic networks, there might be more specific dynamic change cases other than the cases covered by our method, so that we might need to dig into more specific other dynamic cases and find out the solutions.

Includes members:
  Member "a real time detecting algorithm for tracking community structure of dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamo dynamic community detection by incrementally maximizing modularity")
  Member "objective criteria for the evaluation of clustering methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank")

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)