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: "community extraction in multilayer networks with heterogeneous community structure"
  Abstract: Multilayer networks are a useful way to capture and model multiple, binary or weighted relationships among a fixed group of objects. While community detection has proven to be a useful exploratory technique for the analysis of single-layer networks, the development of community detection methods for multilayer networks is still in its infancy. We propose and investigate a procedure, called Multilayer Extraction, that identifies densely connected vertex-layer sets in multilayer networks. Multilayer Extraction makes use of a significance based score that quantifies the connectivity of an observed vertex-layer set through comparison with a fixed degree random graph model. Multilayer Extraction directly handles networks with heterogeneous layers where community structure may be different from layer to layer. The procedure can capture overlapping communities, as well as background vertex-layer pairs that do not belong to any community. We establish consistency of the vertex-layer set optimizer of our proposed multilayer score under the multilayer stochastic block model. We investigate the performance of Multilayer Extraction on three applications and a test bed of simulations. Our theoretical and numerical evaluations suggest that Multilayer Extraction is an effective exploratory tool for analyzing complex multilayer networks. Publicly available R software for Multilayer Extraction is available at this https URL

Connected through hyperedge HE-1 (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"
    - "principled multilayer network embedding"
    - "a survey on network embedding"
    - "poincar e embeddings for learning hierarchical representations"
    - "representation learning for scale free networks"

Connected through hyperedge HE-2 (induced by source paper "multiplex communities and the emergence of international conflict"):
  with members:
    - "identification interpretability and bayesian word embeddings"
    - "hiding individuals and communities in a social network"
    - "understanding state preferences with text as data introducing the un general debate corpus"
    - "the ground truth about metadata and community detection in networks"
    - "higher order organization of complex networks"
    - "learning scalable deep kernels with recurrent structure"
    - "a new framework for dynamical models on multiplex networks"
    - "efficient estimation of word representations in vector space"

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"
    - "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"
    - "clustering with multi layer graphs a spectral perspective"

Connected through hyperedge HE-4 (induced by source paper "community detection and improved detectability in multiplex networks"):
  with members:
    - "detectability thresholds of general modular graphs"
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "finding overlapping communities in multiplex networks"
    - "structure and inference in annotated networks"
    - "clustering network layers with the strata multilayer stochastic block model"
    - "community detection in networks with unequal groups"
    - "information diffusion of topic propagation in social media"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-5 (induced by source paper "an efficient framework for computing structure and semantics preserving community in a heterogeneous multilayer network"):
  with members:
    - "organizational chart inference"
    - "a survey of heterogeneous information network analysis"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-6 (induced by source paper "making a case for mlns for data driven analysis modeling efficiency and versatility"):
  with members:
    - "structure preserving community in a multilayer network definition detection and analysis"
    - "a survey of heterogeneous information network analysis"
    - "querying knowledge graphs by example entity tuples"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-7 (induced by source paper "hierarchical stochastic block model for community detection in multiplex networks"):
  with members:
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "community detection in multiplex networks using locally adaptive random walks"
    - "producing a unified graph representation from multiple social network views"
    - "a degree centrality in multi layered social network"

Connected through hyperedge HE-8 (induced by source paper "fast embedding of multilayer networks an algorithm and application to group fmri"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graph embedding techniques applications and performance a survey"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "clustering network layers with the strata multilayer stochastic block model"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"

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)