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: "the many facets of community detection in complex networks"
  Abstract: Community detection, the decomposition of a graph into essential building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of assortative community structure and classified based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different goals and reasons for why we want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research and points out open directions and avenues for future research.

Connected through hyperedge HE-1 (induced by source paper "embedding based silhouette community detection"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "struc2vec learning node representations from structural identity"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "line large scale information network embedding"

Connected through hyperedge HE-2 (induced by source paper "from free text to clusters of content in health records an unsupervised graph partitioning approach"):
  with members:
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "semantic word clouds with background corpus normalization and t distributed stochastic neighbor embedding"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "document embedding with paragraph vectors"
    - "distributed representations of sentences and documents"
    - "finding role communities in directed networks using role based similarity markov stability and the relaxed minimum spanning tree"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "multiscale dynamical embeddings of complex networks"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "structure and inference in annotated networks"
    - "deepwalk online learning of social representations"
    - "a generalized and adaptive method for community detection"
    - "ranking and clustering of nodes in networks with smart teleportation"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "spectral partitioning of time varying networks with unobserved edges"):
  with members:
    - "blind identification of stochastic block models from dynamical observations"
    - "blind community detection from low rank excitations of a graph filter"
    - "network topology inference from spectral templates"
    - "active sensing of social networks"
    - "network structure inference a survey motivations methods and applications"
    - "topology identification of directed dynamical networks via power spectral analysis"

Connected through hyperedge HE-5 (induced by source paper "modularity belief propagation on multilayer networks to detect significant community structure"):
  with members:
    - "relating modularity maximization and stochastic block models in multilayer networks"
    - "post processing partitions to identify domains of modularity optimization"
    - "the ground truth about metadata and community detection in networks"
    - "graph spectra and the detectability of community structure in networks"

Connected through hyperedge HE-6 (induced by source paper "extracting information from free text through unsupervised graph based clustering an application to patient incident records"):
  with members:
    - "from free text to clusters of content in health records an unsupervised graph partitioning approach"
    - "semantic word clouds with background corpus normalization and t distributed stochastic neighbor embedding"
    - "distributed representations of sentences and documents"
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-7 (induced by source paper "random walks on simplicial complexes and the normalized hodge 1 laplacian"):
  with members:
    - "graph based semi supervised active learning for edge flows"
    - "flow smoothing and denoising graph signal processing in the edge space"
    - "simplicial closure and higher order link prediction"
    - "centralities in simplicial complexes"
    - "centrality measures for graphons accounting for uncertainty in networks"
    - "block models and personalized pagerank"
    - "towards spectral sparsification of simplicial complexes based on generalized effective resistance"
    - "higher order organization of complex networks"

Connected through hyperedge HE-8 (induced by source paper "community structure a comparative evaluation of community detection methods"):
  with members:
    - "an empirical characterization of community structures in complex networks using a bivariate map of quality metrics"
    - "efficient method for estimating the number of communities in a network"
    - "the ground truth about metadata and community detection in networks"
    - "z score based modularity for community detection in networks"
    - "metrics for community analysis a survey"
    - "mixing local and global information for community detection in large networks"
    - "towards linear time overlapping community detection in social networks"
    - "comparative evaluation of community detection algorithms a topological approach"

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)