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 "efficient correlated topic modeling with topic embedding"):
  Abstract: Correlated topic modeling has been limited to small model and problem sizes due to their high computational cost and poor scaling. In this paper, we propose a new model which learns compact topic embeddings and captures topic correlations through the closeness between the topic vectors. Our method enables efficient inference in the low-dimensional embedding space, reducing previous cubic or quadratic time complexity to linear w.r.t the topic size. We further speedup variational inference with a fast sampler to exploit sparsity of topic occurrence. Extensive experiments show that our approach is capable of handling model and data scales which are several orders of magnitude larger than existing correlation results, without sacrificing modeling quality by providing competitive or superior performance in document classification and retrieval.

Includes members:
  Member "on unifying deep generative models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving variational autoencoder with deep feature consistent and generative adversarial training")
  Member "nonparametric variational auto encoders for hierarchical representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effective estimation of deep generative language models")
  Member "recurrent topic transition gan for visual paragraph generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust explanations for visual question answering")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep graph matching consensus")
  Member "joint embedding of hierarchical categories and entities for concept categorization and dataless classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wikipedia2vec an efficient toolkit for learning and visualizing the embeddings of words and entities from wikipedia")
  Member "nonparametric spherical topic modeling with word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "pte predictive text embedding through large scale heterogeneous text networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meta embeddings based on self attention")
  Member "peacock learning long tail topic features for industrial applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "diversity promoting bayesian learning of latent variable models")

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.LG(Machine Learning)