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: "architectural complexity measures of recurrent neural networks"
  Abstract: In this paper, we systematically analyze the connecting architectures of recurrent neural networks (RNNs). Our main contribution is twofold: first, we present a rigorous graph-theoretic framework describing the connecting architectures of RNNs in general. Second, we propose three architecture complexity measures of RNNs: (a) the recurrent depth, which captures the RNN's over-time nonlinear complexity, (b) the feedforward depth, which captures the local input-output nonlinearity (similar to the "depth" in feedforward neural networks (FNNs)), and (c) the recurrent skip coefficient which captures how rapidly the information propagates over time. We rigorously prove each measure's existence and computability. Our experimental results show that RNNs might benefit from larger recurrent depth and feedforward depth. We further demonstrate that increasing recurrent skip coefficient offers performance boosts on long term dependency problems.

Connected through hyperedge HE-1 (induced by source paper "convolutional tensor train lstm for spatio temporal learning"):
  with members:
    - "a tensorized transformer for language modeling"
    - "efficient n dimensional convolutions via higher order factorization"
    - "robust deep networks with randomized tensor regression layers"
    - "scaling autoregressive video models"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "stochastic adversarial video prediction"
    - "stochastic video generation with a learned prior"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-2 (induced by source paper "temporally folded convolutional neural networks for sequence forecasting"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "stcn stochastic temporal convolutional networks"
    - "recurrent neural networks for time series forecasting current status and future directions"
    - "recurrent convolutions for causal 3d cnns"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lstm a search space odyssey"
    - "convolutional image captioning"

Connected through hyperedge HE-3 (induced by source paper "eleatt rnn adding attentiveness to neurons in recurrent neural networks"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "cbam convolutional block attention module"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "deep equilibrium models"):
  with members:
    - "equilibrated recurrent neural network neuronal time delayed self feedback improves accuracy and stability"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "implicit deep learning"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "pytorch an imperative style high performance deep learning library"
    - "recurrent stacking of layers for compact neural machine translation models"
    - "trellis networks for sequence modeling"

Connected through hyperedge HE-5 (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-6 (induced by source paper "multi zone unit for recurrent neural networks"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "a novel aspect guided deep transition model for aspect based sentiment analysis"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "recurrent attention unit"
    - "long short term attention"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "aspect based sentiment analysis with gated convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "gating revisited deep multi layer rnns that can be trained"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "can recurrent neural networks warp time"
    - "the unreasonable effectiveness of the forget gate"
    - "multi temporal land cover classification with sequential recurrent encoders"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

Connected through hyperedge HE-8 (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"
    - "dilated recurrent neural networks"
    - "convolutional sequence to sequence learning"

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.LG(Machine Learning)