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 "community detection and improved detectability in multiplex networks"):
  Abstract: We investigate the widely encountered problem of detecting communities in multiplex networks, such as social networks, with an unknown arbitrary heterogeneous structure. To improve detectability, we propose a generative model that leverages the multiplicity of a single community in multiple layers, with no prior assumption on the relation of communities among different layers. Our model relies on a novel idea of incorporating a large set of generic localized community label constraints across the layers, in conjunction with the celebrated Stochastic Block Model (SBM) in each layer. Accordingly, we build a probabilistic graphical model over the entire multiplex network by treating the constraints as Bayesian priors. We mathematically prove that these constraints/priors promote existence of identical communities across layers without introducing further correlation between individual communities. The constraints are further tailored to render a sparse graphical model and the numerically efficient Belief Propagation algorithm is subsequently employed. We further demonstrate by numerical experiments that in the presence of consistent communities between different layers, consistent communities are matched, and the detectability is improved over a single layer. We compare our model with a "correlated model" which exploits the prior knowledge of community correlation between layers. Similar detectability improvement is obtained under such a correlation, even though our model relies on much milder assumptions than the correlated model. Our model even shows a better detection performance over a certain correlation and signal to noise ratio (SNR) range. In the absence of community correlation, the correlation model naturally fails, while ours maintains its performance.

Includes members:
  Member "detectability thresholds of general modular graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic detectability threshold of the stochastic block model")
  Member "community detection link prediction and layer interdependence in multilayer networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "community detection on mixture multi layer networks via regularized tensor decomposition")
  Member "finding overlapping communities in multiplex networks":
    also connected through other hyperedges:
      (none)
  Member "structure and inference in annotated networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on a scalable entropic breaching of the overfitting barrier in machine learning")
  Member "clustering network layers with the strata multilayer stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hyperbolic multiplex network embedding with maps of random walk")
  Member "community extraction in multilayer networks with heterogeneous community structure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hyperbolic multiplex network embedding with maps of random walk")
  Member "community detection in networks with unequal groups":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self falsifiable hierarchical detection of overlapping communities on social networks")
  Member "information diffusion of topic propagation in social media":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inverse graph learning over optimization 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)