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 "improving language modeling using densely connected recurrent neural networks"):
  Abstract: In this paper, we introduce the novel concept of densely connected layers into recurrent neural networks. We evaluate our proposed architecture on the Penn Treebank language modeling task. We show that we can obtain similar perplexity scores with six times fewer parameters compared to a standard stacked 2-layer LSTM model trained with dropout (Zaremba et al. 2014). In contrast with the current usage of skip connections, we show that densely connecting only a few stacked layers with skip connections already yields significant perplexity reductions.

Includes members:
  Member "tying word vectors and word classifiers a loss framework for language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rethinking batch normalization in transformers")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lossless compression of deep neural networks")
  Member "using the output embedding to improve language models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta embeddings based on self attention")
  Member "pointer sentinel mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "pixel recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "google s neural machine translation system bridging the gap between human and machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "finding function in form compositional character models for open vocabulary word representation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "character aware neural language models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "trends and advancements in deep neural network communication")

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)