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 "dual rectified linear units drelus a replacement for tanh activation functions in quasi recurrent neural networks"):
  Abstract: Abstract   In this paper, we introduce a novel type of Rectified Linear Unit (ReLU), called a Dual Rectified Linear Unit (DReLU). A DReLU, which comes with an unbounded positive and negative image, can be used as a drop-in replacement for a tanh activation function in the recurrent step of Quasi-Recurrent Neural Networks (QRNNs) [1]. Similar to ReLUs, DReLUs are less prone to the vanishing gradient problem, they are noise robust, and they induce sparse activations.  We independently reproduce the QRNN experiments of Bradbury et al. [1] and compare our DReLU-based QRNNs with the original tanh-based QRNNs and Long Short-Term Memory networks (LSTMs) on sentiment classification and word-level language modeling. Additionally, we evaluate on character-level language modeling, showing that we are able to stack up to eight QRNN layers with DReLUs, thus making it possible to improve the current state-of-the-art in character-level language modeling over shallow architectures based on LSTMs.

Includes members:
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lossless compression of deep neural networks")
  Member "quasi recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "recurrent highway networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition")
  Member "neural machine translation in linear time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lava nat a non autoregressive translation model with look around decoding and vocabulary attention")
  Member "hierarchical multiscale recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "recurrent batch normalization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rethinking batch normalization in transformers")
  Member "a simple way to initialize recurrent networks of rectified linear units":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "π nets deep polynomial neural networks")

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)