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: "dilated recurrent neural networks"
  Abstract: Learning with recurrent neural networks (RNNs) on long sequences is a notoriously difficult task. There are three major challenges: 1) complex dependencies, 2) vanishing and exploding gradients, and 3) efficient parallelization. In this paper, we introduce a simple yet effective RNN connection structure, the DilatedRNN, which simultaneously tackles all of these challenges. The proposed architecture is characterized by multi-resolution dilated recurrent skip connections and can be combined flexibly with diverse RNN cells. Moreover, the DilatedRNN reduces the number of parameters needed and enhances training efficiency significantly, while matching state-of-the-art performance (even with standard RNN cells) in tasks involving very long-term dependencies. To provide a theory-based quantification of the architecture's advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures. We rigorously prove the advantages of the DilatedRNN over other recurrent neural architectures. The code for our method is publicly available at this https URL

Connected through hyperedge HE-1 (induced by source paper "improving the gating mechanism of recurrent neural networks"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "transformer xl attentive language models beyond a fixed length context"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "fast parametric learning with activation memorization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "towards binary valued gates for robust lstm training"
    - "optimizing agent behavior over long time scales by transporting value"
    - "trellis networks for sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "state regularized recurrent neural networks"):
  with members:
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "automatic rule extraction from long short term memory networks"

Connected through hyperedge HE-3 (induced by source paper "quantifying long range dependence in language and user behavior to improve rnns"):
  with members:
    - "towards neural mixture recommender for long range dependent user sequences"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "rnns evolving on an equilibrium manifold a panacea for vanishing and exploding gradients"):
  with members:
    - "recurrent neural networks in the eye of differential equations"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "frage frequency agnostic word representation"
    - "can recurrent neural networks warp time"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "neural ordinary differential equations"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "equilibrated recurrent neural network neuronal time delayed self feedback improves accuracy and stability"):
  with members:
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "approximating real time recurrent learning with random kronecker factors"
    - "reviving and improving recurrent back propagation"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "reversible recurrent neural networks"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "simple recurrent units for highly parallelizable recurrence"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "r transformer recurrent neural network enhanced transformer"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "character level language modeling with deeper self attention"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "learning problem agnostic speech representations from multiple self supervised tasks"):
  with members:
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "a survey of recent dnn architectures on the timit phone recognition task"
    - "unsupervised representation learning by predicting image rotations"
    - "representation learning with contrastive predictive coding"
    - "towards a universal neural network encoder for time series"
    - "objects that sound"
    - "learning sight from sound ambient sound provides supervision for visual learning"
    - "unsupervised learning of semantic audio representations"

Connected through hyperedge HE-8 (induced by source paper "learning to remember more with less memorization"):
  with members:
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "robust and scalable differentiable neural computer for question answering"
    - "dual memory neural computer for asynchronous two view sequential learning"
    - "neural speed reading via skim rnn"
    - "attention is all you need"
    - "learning to skim text"
    - "unbounded cache model for online language modeling with open vocabulary"
    - "adaptive computation time for recurrent neural networks"

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.AI(Artificial Intelligence)