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: "triangles to capture social cohesion"
  Abstract: Although community detection has drawn tremendous amount of attention across the sciences in the past decades, no formal consensus has been reached on the very nature of what qualifies a community as such. In this article we take an orthogonal approach by introducing a novel point of view to the problem of overlapping communities. Instead of quantifying the quality of a set of communities, we choose to focus on the intrinsic community-ness of one given set of nodes. To do so, we propose a general metric on graphs, the cohesion, based on counting triangles and inspired by well established sociological considerations. The model has been validated through a large-scale online experiment called Fellows in which users were able to compute their social groups on Face- book and rate the quality of the obtained groups. By observing those ratings in relation to the cohesion we assess that the cohesion is a strong indicator of users subjective perception of the community-ness of a set of people.

Connected through hyperedge HE-1 (induced by source paper "higher order graph convolutional networks"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "motifs in temporal networks"
    - "gated graph sequence neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "minimal gated unit for recurrent neural networks"
    - "gram graph based attention model for healthcare representation learning"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-2 (induced by source paper "triangles as basis to detect communities an application to twitter s network"):
  with members:
    - "using triangles to improve community detection in directed networks"
    - "community detection in networks with node attributes"
    - "empirical comparison of algorithms for network community detection"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-3 (induced by source paper "predicting links in ego networks using temporal information"):
  with members:
    - "topological properties and temporal dynamics of place networks in urban environments"
    - "detecting friendship within dynamic online interaction networks"
    - "predictors of short term decay of cell phone contacts in a large scale communication network"

Connected through hyperedge HE-4 (induced by source paper "a survey and taxonomy of graph sampling"):
  with members:
    - "identifying communities and key vertices by reconstructing networks from samples"
    - "graph size estimation"
    - "coarse grained topology estimation via graph sampling"
    - "towards unbiased bfs sampling"
    - "multigraph sampling of online social networks"
    - "estimating and sampling graphs with multidimensional random walks"
    - "twice ramanujan sparsifiers"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "finding overlapping communities in social networks toward a rigorous approach"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "low distortion inference of latent similarities from a multiplex social network"
    - "the hitchhiker s guide to affiliation networks a game theoretic approach"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)