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 "multilayer spectral graph clustering via convex layer aggregation"):
  Abstract: Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. New challenges arise in multilayer graph clustering for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a theoretical framework for multilayer spectral graph clustering of the nodes via convex layer aggregation. Under a novel multilayer signal plus noise model, we provide a phase transition analysis that establishes the existence of a critical value on the noise level that permits reliable cluster separation. The analysis also specifies analytical upper and lower bounds on the critical value, where the bounds become exact when the clusters have identical sizes. Numerical experiments on synthetic multilayer graphs are conducted to validate the phase transition analysis and study the effect of layer weights and noise levels on clustering reliability.

Includes members:
  Member "phase transitions and a model order selection criterion for spectral graph clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scalable spectral clustering using random binning features")
  Member "clustering network layers with the strata multilayer stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hyperbolic multiplex network embedding with maps of random walk")
  Member "information extraction from larger multi layer social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "latent heterogeneous multilayer community detection")
  Member "principal patterns on graphs discovering coherent structures in datasets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "anomaly detection in the dynamics of web and social networks")
  Member "universal phase transition in community detectability under a stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "phase transitions and a model order selection criterion for spectral graph clustering")
  Member "multi centrality graph spectral decompositions and their application to cyber intrusion detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relative hausdorff distance for network analysis")
  Member "deep community detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "incremental eigenpair computation for graph laplacian matrices theory and applications")
  Member "dynamic stochastic blockmodels for time evolving social networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "variational graph recurrent neural 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.LG(Machine Learning)