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: "nonparametric multi group membership model for dynamic networks"
  Abstract: Relational data-like graphs, networks, and matrices-is often dynamic, where the relational structure evolves over time. A fundamental problem in the analysis of time-varying network data is to extract a summary of the common structure and the dynamics of the underlying relations between the entities. Here we build on the intuition that changes in the network structure are driven by the dynamics at the level of groups of nodes. We propose a nonparametric multi-group membership model for dynamic networks. Our model contains three main components: We model the birth and death of individual groups with respect to the dynamics of the network structure via a distance dependent Indian Buffet Process. We capture the evolution of individual node group memberships via a Factorial Hidden Markov model. And, we explain the dynamics of the network structure by explicitly modeling the connectivity structure of groups. We demonstrate our model's capability of identifying the dynamics of latent groups in a number of different types of network data. Experimental results show that our model provides improved predictive performance over existing dynamic network models on future network forecasting and missing link prediction.

Connected through hyperedge HE-1 (induced by source paper "query based interactive recommendation by meta path and adapted attention gru"):
  with members:
    - "exact k recommendation via maximal clique optimization"
    - "addressing the item cold start problem by attribute driven active learning"
    - "a brand level ranking system with the customized attention gru model"
    - "wide deep learning for recommender systems"
    - "session based recommendations with recurrent neural networks"
    - "attention based models for speech recognition"
    - "recurrent models of visual attention"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (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"
    - "stochastic block transition models for dynamic networks"
    - "multiplicative attribute graph model of real world networks"

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"
    - "stochastic block transition models 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"
    - "stochastic block transition models for dynamic networks"

Connected through hyperedge HE-5 (induced by source paper "a brand level ranking system with the customized attention gru model"):
  with members:
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "improved recurrent neural networks for session based recommendations"
    - "session based recommendations with recurrent neural networks"
    - "attention based models for speech recognition"
    - "recurrent models of visual attention"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-6 (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"
    - "stochastic block transition models for dynamic networks"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (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"
    - "dynamic stochastic blockmodels statistical models for time evolving networks"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

Connected through hyperedge HE-8 (induced by source paper "snap a general purpose network analysis and graph mining library"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "ringo interactive graph analytics on big memory machines"
    - "network lasso clustering and optimization in large graphs"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "community detection in networks with node attributes"
    - "discovering social circles in ego networks"
    - "structure and dynamics of information pathways in online media"
    - "latent multi group membership graph model"

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)