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 on multi layer graphs via subspace analysis on grassmann manifolds"
  Abstract: Relationships between entities in datasets are often of multiple nature, like geographical distance, social relationships, or common interests among people in a social network, for example. This information can naturally be modeled by a set of weighted and undirected graphs that form a global multi-layer graph, where the common vertex set represents the entities and the edges on different layers capture the similarities of the entities in term of the different modalities. In this paper, we address the problem of analyzing multi-layer graphs and propose methods for clustering the vertices by efficiently merging the information provided by the multiple modalities. To this end, we propose to combine the characteristics of individual graph layers using tools from subspace analysis on a Grassmann manifold. The resulting combination can then be viewed as a low dimensional representation of the original data which preserves the most important information from diverse relationships between entities. As an illustrative application of our framework, we use our algorithm in clustering methods and test its performance on several synthetic and real world datasets where it is shown to be superior to baseline schemes and competitive to state-of-the-art techniques. Our generic framework further extends to numerous analysis and learning problems that involve different types of information on graphs.

Connected through hyperedge HE-1 (induced by source paper "learning graphs from data a signal representation perspective"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "graph learning from filtered signals graph system and diffusion kernel identification"
    - "graph learning under sparsity priors"
    - "blind identification of graph filters"
    - "network inference via the time varying graphical lasso"
    - "graph based transform coding with application to image compression"
    - "signal processing on graphs causal modeling of un structured data"
    - "network topology inference from spectral templates"

Connected through hyperedge HE-2 (induced by source paper "structure fusion based on graph convolutional networks for semi supervised classification"):
  with members:
    - "transfer feature generating networks with semantic classes structure for zero shot learning"
    - "semi supervised learning on graph with an alternating diffusion process"
    - "multi gcn graph convolutional networks for multi view networks with applications to global poverty"
    - "a comprehensive survey on graph neural networks"
    - "on filter size in graph convolutional networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "class label autoencoder for zero shot learning"

Connected through hyperedge HE-3 (induced by source paper "neither global nor local a hierarchical robust subspace clustering for image data"):
  with members:
    - "robust subspace clustering by cauchy loss function"
    - "subspace clustering by block diagonal representation"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "robust and efficient subspace segmentation via least squares regression"
    - "learning transformations for clustering and classification"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"

Connected through hyperedge HE-4 (induced by source paper "multi gcn graph convolutional networks for multi view networks with applications to global poverty"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "revisiting semi supervised learning with graph embeddings"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "a survey on multi view learning"

Connected through hyperedge HE-5 (induced by source paper "community detection in multiplex networks"):
  with members:
    - "overlapping community detection by local decentralised vertex centred process"
    - "node centric community detection in multilayer networks with layer coverage diversification bias"
    - "mapping higher order network flows in memory and multilayer networks with infomap"
    - "clustering attributed graphs models measures and methods"
    - "community detection in multiplex networks using locally adaptive random walks"
    - "a local perspective on community structure in multilayer networks"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-6 (induced by source paper "mask combination of multi layer graphs for global structure inference"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "a recurrent graph neural network for multi relational data"
    - "stationary signal processing on graphs"
    - "network topology inference from spectral templates"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "multi modal image retrieval with random walk on multi layer graphs"
    - "learning laplacian matrix in smooth graph signal representations"
    - "combinatorial analysis of multiple networks"

Connected through hyperedge HE-7 (induced by source paper "manifold optimisation assisted gaussian variational approximation"):
  with members:
    - "manopt a matlab toolbox for optimization on manifolds"
    - "adadelta an adaptive learning rate method"
    - "matrix completion from a few entries"

Connected through hyperedge HE-8 (induced by source paper "scalable and robust sparse subspace clustering using randomized clustering and multilayer graphs"):
  with members:
    - "active orthogonal matching pursuit for sparse subspace clustering"
    - "efficient sparse subspace clustering by nearest neighbour filtering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "provable deterministic leverage score sampling"
    - "robust and efficient subspace segmentation via least squares regression"

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)