human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "the dynamic embedded topic model"):
  Abstract: Topic modeling analyzes documents to learn meaningful patterns of words. For documents collected in sequence, dynamic topic models capture how these patterns vary over time. We develop the dynamic embedded topic model (D-ETM), a generative model of documents that combines dynamic latent Dirichlet allocation (D-LDA) and word embeddings. The D-ETM models each word with a categorical distribution parameterized by the inner product between the word embedding and a per-time-step embedding representation of its assigned topic. The D-ETM learns smooth topic trajectories by defining a random walk prior over the embedding representations of the topics. We fit the D-ETM using structured amortized variational inference with a recurrent neural network. On three different corpora---a collection of United Nations debates, a set of ACL abstracts, and a dataset of Science Magazine articles---we found that the D-ETM outperforms D-LDA on a document completion task. We further found that the D-ETM learns more diverse and coherent topics than D-LDA while requiring significantly less time to fit.

Includes members:
  Member "topic modeling in embedding spaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cate category name guidedword embedding")
  Member "improving topic models with latent feature word representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "distilled wasserstein learning for word embedding and topic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "metalda a topic model that efficiently incorporates meta information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "topic modeling in embedding spaces")
  Member "jointly learning word embeddings and latent topics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "conversational structure aware and context sensitive topic model for online discussions")
  Member "understanding state preferences with text as data introducing the un general debate corpus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "detecting local insights from global labels supervised zero shot sequence labeling via a convolutional decomposition")
  Member "nonparametric spherical topic modeling with word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "neural variational inference for text processing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")

Question: Please tell me which class the source paper 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.CL(Computation and Language)