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 "link prediction in multiplex networks based on interlayer similarity"):
  Abstract: Some networked systems can be better modelled by multilayer structure where the individual nodes develop relationships in multiple layers. Multilayer networks with similar nodes across layers are also known as multiplex networks. This manuscript proposes a novel framework for predicting forthcoming or missing links in multiplex networks. The link prediction problem in multiplex networks is how to predict links in one of the layers, taking into account the structural information of other layers. The proposed link prediction framework is based on interlayer similarity and proximity-based features extracted from the layer for which the link prediction is considered. To this end, commonly used proximity-based features such as Adamic-Adar and Jaccard Coefficient are considered. These features that have been originally proposed to predict missing links in monolayer networks, do not require learning, and thus are simple to compute. The proposed method introduces a systematic approach to take into account interlayer similarity for the link prediction purpose. Experimental results on both synthetic and real multiplex networks reveal the effectiveness of the proposed method and show its superior performance than state-of-the-art algorithms proposed for the link prediction problem in multiplex networks.

Includes members:
  Member "qanet tensor decomposition approach for query based anomaly detection in heterogeneous information networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "providing a link prediction model based on structural and homophily similarity in social networks")
  Member "a multilayer approach to multiplexity and link prediction in online geo social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "simulation and augmentation of social networks for building deep learning models")
  Member "spreading processes in multilayer networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks")
  Member "a probabilistic model to resolve diversity accuracy challenge of recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the graph based broad behavior aware recommendation system for interactive news")
  Member "multiple routes transmitted epidemics on multiplex networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "information diffusion of topic propagation in social media")
  Member "diffusion of innovations over multiplex social networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spreading processes in multilayer networks")
  Member "combinatorial analysis of multiple networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "supracentrality analysis of temporal networks with directed interlayer coupling")

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)