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: "finding role communities in directed networks using role based similarity markov stability and the relaxed minimum spanning tree"
  Abstract: We present a framework to cluster nodes in directed networks according to their roles by combining Role-Based Similarity (RBS) and Markov Stability, two techniques based on flows. First we compute the RBS matrix, which contains the pairwise similarities between nodes according to the scaled number of in- and out-directed paths of different lengths. The weighted RBS similarity matrix is then transformed into an undirected similarity network using the Relaxed Minimum-Spanning Tree (RMST) algorithm, which uses the geometric structure of the RBS matrix to unblur the network, such that edges between nodes with high, direct RBS are preserved. Finally, we partition the RMST similarity network into role-communities of nodes at all scales using Markov Stability to find a robust set of roles in the network. We showcase our framework through a biological and a man-made network.

Connected through hyperedge HE-1 (induced by source paper "semi supervised classification on graphs using explicit diffusion dynamics"):
  with members:
    - "multiscale dynamical embeddings of complex networks"
    - "quantifying the alignment of graph and features in deep learning"
    - "graph based data clustering via multiscale community detection"
    - "geometric multiscale community detection markov stability and vector partitioning"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "large scale learnable graph convolutional networks"
    - "geometric deep learning going beyond euclidean data"

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"
    - "the many facets of community detection in complex networks"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "document embedding with paragraph vectors"
    - "distributed representations of sentences and documents"
    - "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 "graph based data clustering via multiscale community detection"):
  with members:
    - "multiscale dynamical embeddings of complex networks"
    - "from free text to clusters of content in health records an unsupervised graph partitioning approach"
    - "scale variant topological information for characterizing the structure of complex networks"
    - "geometric deep learning going beyond euclidean data"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "spectral clustering algorithms for the detection of clusters in block cyclic and block acyclic graphs"):
  with members:
    - "detecting highly cyclic structure with complex eigenpairs"
    - "clustering and community detection in directed networks a survey"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "data driven unsupervised clustering of online learner behaviour"):
  with members:
    - "likely to stop predicting stopout in massive open online courses"
    - "an empirical evaluation of similarity measures for time series classification"

Connected through hyperedge HE-6 (induced by source paper "detecting highly cyclic structure with complex eigenpairs"):
  with members:
    - "tensor spectral clustering for partitioning higher order network structures"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "using triangles to improve community detection in directed networks"
    - "clustering and community detection in directed networks a survey"
    - "triadic measures on graphs the power of wedge sampling"
    - "directed closure measures for networks with reciprocity"
    - "scalable spectral algorithms for community detection in directed networks"

Connected through hyperedge HE-7 (induced by source paper "community detection and role identification in directed networks understanding the twitter network of the care data debate"):
  with members:
    - "clustering and community detection in directed networks a survey"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

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)