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: "stochastic block transition models for dynamic networks"
  Abstract: There has been great interest in recent years on statistical models for dynamic networks. In this paper, I propose a stochastic block transition model (SBTM) for dynamic networks that is inspired by the well-known stochastic block model (SBM) for static networks and previous dynamic extensions of the SBM. Unlike most existing dynamic network models, it does not make a hidden Markov assumption on the edge-level dynamics, allowing the presence or absence of edges to directly influence future edge probabilities while retaining the interpretability of the SBM. I derive an approximate inference procedure for the SBTM and demonstrate that it is significantly better at reproducing durations of edges in real social network data.

Connected through hyperedge HE-1 (induced by source paper "a statistical model for dynamic networks with neural variational inference"):
  with members:
    - "a generative model for dynamic networks with applications"
    - "adam a method for stochastic optimization"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "nonparametric multi group membership model for dynamic networks"
    - "multiplicative attribute graph model of real world networks"

Connected through hyperedge HE-2 (induced by source paper "consistent community detection in continuous time networks of relational events"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "the block point process model for continuous time event based dynamic networks"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "modeling events with cascades of poisson processes"

Connected through hyperedge HE-3 (induced by source paper "sequential edge clustering in temporal multigraphs"):
  with members:
    - "a poisson gamma probabilistic model for latent node group memberships in dynamic networks"
    - "dynamic network model from partial observations"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "nonparametric multi group membership model for dynamic networks"

Connected through hyperedge HE-4 (induced by source paper "dynamic nonparametric edge clustering model for time evolving sparse networks"):
  with members:
    - "a poisson gamma probabilistic model for latent node group memberships in dynamic networks"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "nonparametric multi group membership model for dynamic networks"

Connected through hyperedge HE-5 (induced by source paper "models for capturing temporal smoothness in evolving networks for learning latent representation of nodes"):
  with members:
    - "motifs in temporal networks"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-6 (induced by source paper "dylink2vec effective feature representation for link prediction in dynamic networks"):
  with members:
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "deepwalk online learning of social representations"
    - "link prediction in social networks the state of the art"
    - "nonparametric link prediction in dynamic networks"

Connected through hyperedge HE-7 (induced by source paper "evolving latent space model for dynamic networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "nonparametric multi group membership model for dynamic networks"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "detecting topological changes in dynamic community networks"):
  with members:
    - "a survey on social media anomaly detection"
    - "the resistance perturbation distance a metric for the analysis of dynamic networks"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "communities and hierarchical structures in dynamic social networks analysis and visualization"
    - "anomaly detection in dynamic networks of varying size"
    - "exact recovery in the stochastic block model"
    - "intrinsically dynamic network communities"

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)