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: "principled multilayer network embedding"
  Abstract: Multilayer network analysis has become a vital tool for understanding different relationships and their interactions in a complex system, where each layer in a multilayer network depicts the topological structure of a group of nodes corresponding to a particular relationship. The interactions among different layers imply how the interplay of different relations on the topology of each layer. For a single-layer network, network embedding methods have been proposed to project the nodes in a network into a continuous vector space with a relatively small number of dimensions, where the space embeds the social representations among nodes. These algorithms have been proved to have a better performance on a variety of regular graph analysis tasks, such as link prediction, or multi-label classification. In this paper, by extending a standard graph mining into multilayer network, we have proposed three methods ("network aggregation," "results aggregation" and "layer co-analysis") to project a multilayer network into a continuous vector space. From the evaluation, we have proved that comparing with regular link prediction methods, "layer co-analysis" achieved the best performance on most of the datasets, while "network aggregation" and "results aggregation" also have better performance than regular link prediction methods.

Connected through hyperedge HE-1 (induced by source paper "aligraph a comprehensive graph neural network platform"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graph networks as learnable physics engines for inference and control"
    - "graph embedding techniques applications and performance a survey"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "attributed social network embedding"
    - "one shot relational learning for knowledge graphs"
    - "struc2vec learning node representations from structural identity"
    - "an attention based collaboration framework for multi view network representation learning"

Connected through hyperedge HE-2 (induced by source paper "representation learning for attributed multiplex heterogeneous network"):
  with members:
    - "netsmf large scale network embedding as sparse matrix factorization"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "attributed social network embedding"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "an attention based collaboration framework for multi view network representation learning"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for recommendation"

Connected through hyperedge HE-3 (induced by source paper "hyperbolic multiplex network embedding with maps of random walk"):
  with members:
    - "multi relational poincar e graph embeddings"
    - "representation learning for attributed multiplex heterogeneous network"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "an attention based collaboration framework for multi view network representation learning"
    - "a survey on network embedding"
    - "poincar e embeddings for learning hierarchical representations"
    - "representation learning for scale free networks"
    - "a unified framework for community detection and network representation learning"

Connected through hyperedge HE-4 (induced by source paper "unsupervised attributed multiplex network embedding"):
  with members:
    - "self attention graph pooling"
    - "lovasz convolutional networks"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "multi dimensional graph convolutional networks"
    - "mine mutual information neural estimation"
    - "struc2vec learning node representations from structural identity"
    - "an attention based collaboration framework for multi view network representation learning"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-5 (induced by source paper "mvn2vec preservation and collaboration in multi view network embedding"):
  with members:
    - "representation learning for attributed multiplex heterogeneous network"
    - "a structural representation learning for multi relational networks"
    - "struc2vec learning node representations from structural identity"
    - "an attention based collaboration framework for multi view network representation learning"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "don t walk skip online learning of multi scale network embeddings"

Connected through hyperedge HE-6 (induced by source paper "mile a multi level framework for scalable graph embedding"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "harp hierarchical representation learning for networks"
    - "semi supervised embedding in attributed networks with outliers"
    - "node2vec scalable feature learning for networks"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "deep convolutional networks on graph structured data"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-7 (induced by source paper "incremental eigenpair computation for graph laplacian matrices theory and applications"):
  with members:
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "name disambiguation in anonymized graphs using network embedding"
    - "phase transitions and a model order selection criterion for spectral graph clustering"
    - "nous construction and querying of dynamic knowledge graphs"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "trust from the past bayesian personalized ranking based link prediction in knowledge graphs"
    - "spectral detection in the censored block model"

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.SI(Social and Information Networks)