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 "dyperm maximizing permanence for dynamic community detection"):
  Abstract: In this paper, we propose DyPerm, the first dynamic community detection method which optimizes a novel community scoring metric, called permanence. DyPerm incrementally modifies the community structure by updating those communities where the editing of nodes and edges has been performed, keeping the rest of the network unchanged. We present strong theoretical guarantees to show how/why mere updates on the existing community structure leads to permanence maximization in dynamic networks, which in turn decreases the computational complexity drastically. Experiments on both synthetic and six real-world networks with given ground-truth community structure show that DyPerm achieves (on average) 35% gain in accuracy (based on NMI) compared to the best method among four baseline methods. DyPerm also turns out to be 15 times faster than its static counterpart.

Includes members:
  Member "modularity based dynamic community detection":
    also connected through other hyperedges:
      (none)
  Member "permanence and community structure in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "correlation based community detection")
  Member "metrics for community analysis a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "when less is more systematic analysis of cascade based community detection")
  Member "a real time detecting algorithm for tracking community structure of dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamo dynamic community detection by incrementally maximizing modularity")
  Member "overlapping community detection in networks the state of the art and comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning with relational information for short sequences")
  Member "labelrankt incremental community detection in dynamic networks via label propagation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a fast and efficient incremental approach toward dynamic community detection")
  Member "intrinsically dynamic network communities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ensemble based discovery of disjoint overlapping and fuzzy community structures in networks")

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)