human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "clustering with multi layer graphs a spectral perspective"
  Abstract: Observational data usually comes with a multimodal nature, which means that it can be naturally represented by a multi-layer graph whose layers share the same set of vertices (objects) with different edges (pairwise relationships). In this paper, we address the problem of combining different layers of the multi-layer graph for an improved clustering of the vertices compared to using layers independently. We propose two novel methods, which are based on a joint matrix factorization and a graph regularization framework respectively, to efficiently combine the spectrum of the multiple graph layers, namely the eigenvectors of the graph Laplacian matrices. In each case, the resulting combination, which we call a “joint spectrum” of multiple layers, is used for clustering the vertices. We evaluate our approaches by experiments with several real world social network datasets. Results demonstrate the superior or competitive performance of the proposed methods compared to state-of-the-art techniques and common baseline methods, such as co-regularization and summation of information from individual graphs.

Connected through hyperedge HE-1 (induced by source paper "community detection on mixture multi layer networks via regularized tensor decomposition"):
  with members:
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "a statistical model for tensor pca"
    - "most tensor problems are np hard"
    - "matrix completion from a few entries"

Connected through hyperedge HE-2 (induced by source paper "vgraph a generative model for joint community detection and node representation learning"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "communitygan community detection with generative adversarial nets"
    - "verse versatile graph embeddings from similarity measures"
    - "gemsec graph embedding with self clustering"
    - "a unified framework for community detection and network representation learning"
    - "node2vec scalable feature learning for networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "line large scale information network embedding"

Connected through hyperedge HE-3 (induced by source paper "structure preserving community in a multilayer network definition detection and analysis"):
  with members:
    - "generic multilayer network data analysis with the fusion of content and structure"
    - "community extraction in multilayer networks with heterogeneous community structure"
    - "modularity in complex multilayer networks with multiple aspects a static perspective"
    - "organizational chart inference"
    - "a survey of heterogeneous information network analysis"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "querying knowledge graphs by example entity tuples"
    - "mining attribute structure correlated patterns in large attributed graphs"

Connected through hyperedge HE-4 (induced by source paper "efficient community detection in boolean composed multiplex networks"):
  with members:
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "modularity in complex multilayer networks with multiple aspects a static perspective"
    - "generalized measures for the evaluation of community detection methods"
    - "networkit an interactive tool suite for high performance network analysis"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "mining attribute structure correlated patterns in large attributed graphs"

Connected through hyperedge HE-5 (induced by source paper "graph signal processing part i graphs graph spectra and spectral clustering"):
  with members:
    - "discrete signal processing on graphs frequency analysis"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-6 (induced by source paper "diversified coherent core search on multi layer graphs"):
  with members:
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "efficient community detection in large networks using content and links"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-7 (induced by source paper "phase transitions and a model order selection criterion for spectral graph clustering"):
  with members:
    - "estimating the number of communities in a network"
    - "spectral detection in the censored block model"
    - "universal phase transition in community detectability under a stochastic block model"
    - "local set based graph signal reconstruction"
    - "incremental method for spectral clustering of increasing orders"
    - "signal recovery on graphs variation minimization"
    - "multi layer graph analysis for dynamic social networks"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-8 (induced by source paper "amos an automated model order selection algorithm for spectral graph clustering"):
  with members:
    - "phase transitions and a model order selection criterion for spectral graph clustering"
    - "spectral detection in the censored block model"
    - "local set based graph signal reconstruction"
    - "signal recovery on graphs variation minimization"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "multi layer graph analysis for dynamic social networks"
    - "phase transitions in spectral community detection"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Question: Please tell me which class the center node 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)