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 "structure preserving community in a multilayer network definition detection and analysis"):
  Abstract: Multilayer networks or MLNs (also called multiplexes or network of networks) are being used extensively for modeling and analysis of data sets with multiple entity and feature types as well as their relationships. As the concept of communities and hubs are used for these analysis, a structure-preserving definition for them on MLNs (that retains the original MLN structure and node/edge labels and types) and its efficient detection are critical. There is no structure-preserving definition of a community for a MLN as most of the current analyses aggregate a MLN to a single graph. Although there is consensus on community definition for single graphs (and detection packages) and to a lesser extent for homogeneous MLNs, it is lacking for heterogeneous MLNs. In this paper, we not only provide a structure-preserving definition for the first time, but also its efficient computation using a decoupling approach, and discuss its characteristics & significance for analysis. The proposed decoupling approach for efficiency combines communities from individual layers to form a serial k-community for connected k layers in a MLN. We propose several weight metrics for composing layer-wise communities using the bipartite graph match approach based on the analysis semantics. Our proposed approach has a number of advantages. It: i) leverages extant single graph community detection algorithms, ii) is based on the widely-used maximal flow bipartite graph matching for composing k layers, iii) introduces several weight metrics that are customized for the community concept, and iv) experimentally validates the definition, mapping, and efficiency from a flexible analysis perspective on widely-used IMDb data set. #R##N#Keywords: Heterogeneous Multilayer Networks; Bipartite Graphs; Community Definition and Detection; Decoupling-Based Composition

Includes members:
  Member "generic multilayer network data analysis with the fusion of content and structure":
    also connected through other hyperedges:
      (none)
  Member "community extraction in multilayer networks with heterogeneous community structure":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hyperbolic multiplex network embedding with maps of random walk")
  Member "modularity in complex multilayer networks with multiple aspects a static perspective":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hyperbolic multiplex network embedding with maps of random walk")
  Member "organizational chart inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an efficient framework for computing structure and semantics preserving community in a heterogeneous multilayer network")
  Member "a survey of heterogeneous information network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems")
  Member "overlapping community detection in networks the state of the art and comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning with relational information for short sequences")
  Member "querying knowledge graphs by example entity tuples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the ubiquity of large graphs and surprising challenges of graph processing extended survey")
  Member "mining attribute structure correlated patterns in large attributed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "detection of community structures in networks with nodal features based on generative probabilistic approach")

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)