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: "random graph models for dynamic networks"
  Abstract: Recent theoretical work on the modeling of network structure has focused primarily on networks that are static and unchanging, but many real-world networks change their structure over time. There exist natural generalizations to the dynamic case of many static network models, including the classic random graph, the configuration model, and the stochastic block model, where one assumes that the appearance and disappearance of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. Here we give an introduction to this class of models, showing for instance how one can compute their equilibrium properties. We also demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data using the method of maximum likelihood. This allows us, for example, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate these methods with a selection of applications, both to computer-generated test networks and real-world examples.

Connected through hyperedge HE-1 (induced by source paper "bayesian graph convolutional neural networks using node copying"):
  with members:
    - "fisher bures adversary graph convolutional networks"
    - "residual gated graph convnets"
    - "dual primal graph convolutional networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "robust spatial filtering with graph convolutional neural networks"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-2 (induced by source paper "role detection in bicycle sharing networks using multilayer stochastic block models"):
  with members:
    - "relating modularity maximization and stochastic block models in multilayer networks"
    - "community discovery in dynamic networks a survey"
    - "a multi scale analysis of 27 000 urban street networks every us city town urbanized area and zillow neighborhood"
    - "the ground truth about metadata and community detection in networks"
    - "a framework for the construction of generative models for mesoscale structure in multilayer networks"
    - "clustering network layers with the strata multilayer stochastic block model"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "generative benchmark models for mesoscale structures in multilayer networks"

Connected through hyperedge HE-3 (induced by source paper "swnet small world neural networks and rapid convergence"):
  with members:
    - "resnet in resnet generalizing residual architectures"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "size agnostic change point detection framework for evolving networks"):
  with members:
    - "motifs in temporal networks"
    - "fast change point detection on dynamic social networks"
    - "social networks under stress"
    - "detecting change points in the large scale structure of evolving networks"
    - "multi level anomaly detection on time varying graph data"
    - "graph based anomaly detection and description a survey"
    - "deltacon a principled massive graph similarity function"

Connected through hyperedge HE-5 (induced by source paper "fast change point detection on dynamic social networks"):
  with members:
    - "modelling sequences and temporal networks with dynamic community structures"
    - "detecting weak changes in dynamic events over networks"
    - "detecting change points in the large scale structure of evolving networks"
    - "multi level anomaly detection on time varying graph data"
    - "fast generation of large scale social networks with clustering"
    - "community structure and scale free collections of erdős renyi graphs"
    - "netsimile a scalable approach to size independent network similarity"

Connected through hyperedge HE-6 (induced by source paper "a dynamic network model with persistent links and node specific latent variables with an application to the interbank market"):
  with members:
    - "link persistence and conditional distances in multiplex networks"
    - "disentangling group and link persistence in dynamic stochastic block models"
    - "modelling sequences and temporal networks with dynamic community structures"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "stochastic block transition models for dynamic networks"

Connected through hyperedge HE-7 (induced by source paper "a framework for the construction of generative models for mesoscale structure in multilayer networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "the many facets of community detection in complex networks"
    - "the geometric block model"
    - "block models and personalized pagerank"
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "multiresolution consensus clustering in networks"
    - "the ground truth about metadata and community detection in networks"
    - "scout simultaneous time segmentation and community detection in dynamic 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)