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: "continuous time dynamic topic models"
  Abstract: In this paper, we develop the continuous time dynamic topic model (cDTM). The cDTM is a dynamic topic model that uses Brownian motion to model the latent topics through a sequential collection of documents, where a "topic" is a pattern of word use that we expect to evolve over the course of the collection. We derive an efficient variational approximate inference algorithm that takes advantage of the sparsity of observations in text, a property that lets us easily handle many time points. In contrast to the cDTM, the original discrete-time dynamic topic model (dDTM) requires that time be discretized. Moreover, the complexity of variational inference for the dDTM grows quickly as time granularity increases, a drawback which limits fine-grained discretization. We demonstrate the cDTM on two news corpora, reporting both predictive perplexity and the novel task of time stamp prediction.

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"
    - "dynamic poisson factorization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "learning dynamic author representations with temporal language models"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "multiple attribute text style transfer"
    - "deep contextualized word representations"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "construction of the literature graph in semantic scholar"
    - "dynamic word embeddings for evolving semantic discovery"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "the dynamic embedded topic model"):
  with members:
    - "topic modeling in embedding spaces"
    - "improving topic models with latent feature word representations"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "metalda a topic model that efficiently incorporates meta information"
    - "jointly learning word embeddings and latent topics"
    - "understanding state preferences with text as data introducing the un general debate corpus"
    - "nonparametric spherical topic modeling with word embeddings"
    - "neural variational inference for text processing"

Connected through hyperedge HE-4 (induced by source paper "discovering topics with neural topic models built from plsa assumptions"):
  with members:
    - "texttovec deep contextualized neural autoregressive topic models of language with distributed compositional prior"
    - "mixing dirichlet topic models and word embeddings to make lda2vec"
    - "kaggle lshtc4 winning solution"
    - "multilingual topic models for unaligned text"
    - "natural language processing almost from scratch"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-5 (induced by source paper "recurrent point processes for dynamic review models"):
  with members:
    - "review driven answer generation for product related questions in e commerce"
    - "structured neural topic models for reviews"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "transnets learning to transform for recommendation"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "neural variational inference for text processing"

Connected through hyperedge HE-6 (induced by source paper "identifying hidden buyers in darknet markets via dirichlet hawkes process"):
  with members:
    - "a dirichlet mixture model of hawkes processes for event sequence clustering"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "traveling the silk road a measurement analysis of a large anonymous online marketplace"

Connected through hyperedge HE-7 (induced by source paper "tracking the evolution of words with time reflective text representations"):
  with members:
    - "a state of the art of semantic change computation"
    - "dynamic word embeddings for evolving semantic discovery"
    - "learning word relatedness over time"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "bayesian neural word embedding"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "temporal analysis of language through neural language models"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "learning latent events from network message logs a decomposition based approach"):
  with members:
    - "the long and the short of it summarising event sequences with serial episodes"
    - "tensor decompositions for learning latent variable models"

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.IR(Information Retrieval)