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 "clustering evolving networks"):
  Abstract: Roughly speaking, clustering evolving networks aims at detecting structurally dense subgroups in networks that evolve over time. This implies that the subgroups we seek for also evolve, which results in many additional tasks compared to clustering static networks. We discuss these additional tasks and difficulties resulting thereof and present an overview on current approaches to solve these problems. We focus on clustering approaches in online scenarios, i.e., approaches that incrementally use structural information from previous time steps in order to incorporate temporal smoothness or to achieve low running time. Moreover, we describe a collection of real world networks and generators for synthetic data that are often used for evaluation.

Includes members:
  Member "labelrank a stabilized label propagation algorithm for community detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discovering communities of community discovery")
  Member "benchmarking community detection methods on social media data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a testing based extraction algorithm for identifying significant communities in networks")
  Member "labelrankt incremental community detection in dynamic networks via label propagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a fast and efficient incremental approach toward dynamic community detection")
  Member "on graph stream clustering with side information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast memory efficient anomaly detection in streaming heterogeneous graphs")
  Member "real time discovery of dense clusters in highly dynamic graphs identifying real world events in highly dynamic environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mining bursting communities in temporal graphs")
  Member "detect overlapping and hierarchical community structure in networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ensemble based overlapping community detection using disjoint community structures")
  Member "efficient modularity optimization by multistep greedy algorithm and vertex mover refinement":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "correlation based community detection")
  Member "a tutorial on spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating spectral clustering via sampling a review")

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)