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 for time evolving social 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 present a state-space model for dynamic networks that extends the well-known stochastic blockmodel for static networks to the dynamic setting. We fit the model in a near-optimal manner using an extended Kalman filter (EKF) augmented with a local search. We demonstrate that the EKF-based algorithm performs competitively with a state-of-the-art algorithm based on Markov chain Monte Carlo sampling but is significantly less computationally demanding.

Connected through hyperedge HE-1 (induced by source paper "variational graph recurrent neural networks"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "semi implicit graph variational auto encoders"
    - "dyngem deep embedding method for dynamic graphs"
    - "learning structural node embeddings via diffusion wavelets"
    - "dynamic graph representation learning via self attention networks"
    - "struc2vec learning node representations from structural identity"
    - "timers error bounded svd restart on dynamic networks"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"

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"
    - "random graph models for dynamic networks"
    - "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"

Connected through hyperedge HE-3 (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"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "stochastic block transition models for dynamic networks"
    - "nonparametric multi group membership model for dynamic networks"
    - "multiplicative attribute graph model of real world networks"

Connected through hyperedge HE-4 (induced by source paper "latent channel networks"):
  with members:
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "julia a fresh approach to numerical computing"
    - "exact recovery in the stochastic block model"
    - "social structure of facebook networks"
    - "efficient and principled method for detecting communities in networks"

Connected through hyperedge HE-5 (induced by source paper "network interpolation"):
  with members:
    - "simplicial closure and higher order link prediction"
    - "choosing to grow a graph modeling network formation as discrete choice"
    - "a sampling framework for counting temporal motifs"
    - "motifs in temporal networks"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"

Connected through hyperedge HE-6 (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"
    - "stochastic block transition models for dynamic networks"
    - "nonparametric multi group membership model for dynamic networks"

Connected through hyperedge HE-7 (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"
    - "stochastic block transition models for dynamic networks"
    - "nonparametric multi group membership model for dynamic networks"

Connected through hyperedge HE-8 (induced by source paper "community discovery in dynamic networks a survey"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "revealing evolutions in dynamical networks"
    - "the ground truth about metadata and community detection in networks"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "a framework for the construction of generative models for mesoscale structure in multilayer networks"
    - "generative benchmark models for mesoscale structures in multilayer networks"
    - "enumerating maximal cliques in temporal graphs"
    - "using multiple criteria methods to evaluate community partitions"

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)