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 "the continuous configuration model a null for community detection on weighted networks"):
  Abstract: Community detection is the process of grouping strongly connected nodes in a network. Many community detection methods for un-weighted networks have a theoretical basis in a null model, which provides an interpretation of resulting communities in terms of statistical significance. In this paper, we introduce a null for sparse weighted networks called the continuous configuration model. We prove a Central Limit Theorem for sums of edge weights under the model, and propose a community extraction method called CCME which combines this result with an iterative multiple testing framework. To benchmark the method, we provide a simulation framework that incorporates the continuous configuration model as a way to plant null or "background" nodes in weighted networks with communities. We show CCME to be competitive with existing methods in accurately identifying both disjoint and overlapping communities, while being particularly effective in ignoring background nodes when they exist. We present two real-world data sets with potential background nodes and analyze them with CCME, yielding results that correspond to known features of the data.

Includes members:
  Member "overlapping community detection in networks the state of the art and comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learning with relational information for short sequences")
  Member "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self falsifiable hierarchical detection of overlapping communities on social 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)