human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "dyperm maximizing permanence for dynamic community detection"):
  with members:
    - "permanence and community structure in complex networks"
    - "metrics for community analysis a survey"
    - "a real time detecting algorithm for tracking community structure of dynamic networks"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "labelrankt incremental community detection in dynamic networks via label propagation"
    - "intrinsically dynamic network communities"

Question: Please tell me which class the center node 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)