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 poisson factorization"
  Abstract: Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically, the latent factors are assumed to be static and, given these factors, the observed pref- erences and behaviors of users are assumed to be generated without order. These assumptions limit the explorative and predictive capabilities of such models, since users' interests and item popularity may evolve over time. To address this, we propose dPF, a dynamic matrix factorization model based on the recent Poisson factorization model for recommendations. dPF models the time evolving latent factors with a Kalman filter and the actions with Poisson distributions. We derive a scalable variational inference algorithm to infer the latent factors. Finally, we demonstrate dPF on 10 years of user click data from arXiv.org, one of the largest repository of scientific papers and a formidable source of information about the behavior of scientists. Empirically we show performance improvement over both static and, more recently proposed, dynamic recommendation models. We also provide a thorough exploration of the inferred posteriors over the latent variables.

Connected through hyperedge HE-1 (induced by source paper "dynamic transformation of prior knowledge intobayesian models for data streams"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "improving topic models with latent feature word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bayesian models of data streams with hierarchical power priors"
    - "session based recommendations with recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"
    - "continuous time dynamic topic models"

Connected through hyperedge HE-2 (induced by source paper "session based social recommendation via dynamic graph attention networks"):
  with members:
    - "neural message passing for quantum chemistry"
    - "neural attentive session based recommendation"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "session based recommendations with recurrent neural networks"
    - "deep convolutional networks on graph structured data"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-3 (induced by source paper "community detection over a heterogeneous population of non aligned networks"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "link prediction in social networks the state of the art"
    - "nonparametric link prediction in dynamic networks"
    - "a tutorial on spectral clustering"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-4 (induced by source paper "dynamicmf a matrix factorization approach to monitor resource usage in high performance computing systems"):
  with members:
    - "tracking system behaviour from resource usage data"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "fast non bayesian poisson factorization for implicit feedback recommendations"):
  with members:
    - "coupled compound poisson factorization"
    - "fast and simple optimization for poisson likelihood models"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-6 (induced by source paper "recurrent poisson factorization for temporal recommendation"):
  with members:
    - "continuous time user modeling in the presence of badges a probabilistic approach"
    - "spatio temporal modeling of users check ins in location based social networks"
    - "correlated cascades compete or cooperate"
    - "modeling adoption and usage of competing products"
    - "modeling events with cascades of poisson processes"

Connected through hyperedge HE-7 (induced by source paper "deep coevolutionary network embedding user and item features for recommendation"):
  with members:
    - "improved recurrent neural networks for session based recommendations"
    - "general factorization framework for context aware recommendations"
    - "discriminative embeddings of latent variable models for structured data"
    - "session based recommendations with recurrent neural networks"
    - "diamond sampling for approximate maximum all pairs dot product mad search"
    - "adam a method for stochastic optimization"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-8 (induced by source paper "mixing dirichlet topic models and word embeddings to make lda2vec"):
  with members:
    - "contextual lstm clstm models for large scale nlp tasks"
    - "inferring networks of substitutable and complementary products"
    - "skip thought vectors"
    - "adam a method for stochastic optimization"
    - "improving neural networks by preventing co adaptation of feature detectors"

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.LG(Machine Learning)