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: "dynamic stochastic blockmodels statistical models for time evolving networks"
  Abstract: Significant efforts have gone into the development of statistical models for analyzing data in the form of networks, such as social networks. Most existing work has focused on modeling static networks, which represent either a single time snapshot or an aggregate view over time. There has been recent interest in statistical modeling of dynamic networks, which are observed at multiple points in time and offer a richer representation of many complex phenomena. In this paper, we propose a state-space model for dynamic networks that extends the well-known stochastic blockmodel for static networks to the dynamic setting. We then propose a procedure to fit the model using a modification of the extended Kalman filter augmented with a local search. We apply the procedure to analyze a dynamic social network of email communication.

Connected through hyperedge HE-1 (induced by source paper "dynamic network embedding via incremental skip gram with negative sampling"):
  with members:
    - "learning structural node embeddings via diffusion wavelets"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for recommendation"
    - "attributed network embedding for learning in a dynamic environment"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "bayesian estimation of the latent dimension and communities in stochastic blockmodels"):
  with members:
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "estimating the number of communities in a network"
    - "exact recovery in the stochastic block model"
    - "pseudo likelihood methods for community detection in large sparse networks"
    - "clustering and community detection in directed networks a survey"
    - "a tutorial on spectral clustering"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-3 (induced by source paper "stream graphs and link streams for the modeling of interactions over time"):
  with members:
    - "degree based outlier detection within ip traffic modelled as a link stream"
    - "non altering time scales for aggregation of dynamic networks into series of graphs"
    - "movie rating prediction using content based and link stream features"
    - "motifs in temporal networks"
    - "discovering patterns of interest in ip traffic using cliques in bipartite link streams"
    - "time series analysis of temporal networks"
    - "graphprints towards a graph analytic method for network anomaly detection"
    - "semirings for temporal network analysis"

Connected through hyperedge HE-4 (induced by source paper "modelling sequences and temporal networks with dynamic community structures"):
  with members:
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "maps of sparse markov chains efficiently reveal community structure in network flows with memory"
    - "representing higher order dependencies in networks"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "model selection for degree corrected block models"
    - "detecting change points in the large scale structure of evolving networks"
    - "stochastic block transition models for dynamic networks"

Connected through hyperedge HE-5 (induced by source paper "random graph models for dynamic networks"):
  with members:
    - "stability of spreading processes over time varying large scale networks"
    - "clustering network layers with the strata multilayer stochastic block model"
    - "stochastic block transition models for dynamic networks"
    - "nonparametric multi group membership model for dynamic networks"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

Connected through hyperedge HE-6 (induced by source paper "seasonal stochastic blockmodeling for anomaly detection in dynamic networks"):
  with members:
    - "seasonality in dynamic stochastic block models"
    - "stochastic block transition models for dynamic networks"
    - "human dynamics revealed through web analytics"

Connected through hyperedge HE-7 (induced by source paper "seasonality in dynamic stochastic block models"):
  with members:
    - "stochastic block transition models for dynamic networks"
    - "human dynamics revealed through web analytics"

Connected through hyperedge HE-8 (induced by source paper "duality between temporal networks and signals extraction of the temporal network structures"):
  with members:
    - "discovering the structure of complex networks by minimizing cyclic bandwidth sum"
    - "a regularized graph layout framework for dynamic network visualization"
    - "discrete signal processing on graphs frequency analysis"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "detecting overlapping temporal community structure in time evolving networks"
    - "information spreading in stationary markovian evolving graphs"
    - "time varying graphs and 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)