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: "r transformer recurrent neural network enhanced transformer"
  Abstract: Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure. Therefore, many non-recurrent sequence models that are built on convolution and attention operations have been proposed recently. Notably, models with multi-head attention such as Transformer have demonstrated extreme effectiveness in capturing long-term dependencies in a variety of sequence modeling tasks. Despite their success, however, these models lack necessary components to model local structures in sequences and heavily rely on position embeddings that have limited effects and require a considerable amount of design efforts. In this paper, we propose the R-Transformer which enjoys the advantages of both RNNs and the multi-head attention mechanism while avoids their respective drawbacks. The proposed model can effectively capture both local structures and global long-term dependencies in sequences without any use of position embeddings. We evaluate R-Transformer through extensive experiments with data from a wide range of domains and the empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks. We have made the code publicly available at \url{this https URL}.

Connected through hyperedge HE-1 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "reducing transformer depth on demand with structured dropout"
    - "augmenting self attention with persistent memory"
    - "compressive transformers for long range sequence modelling"
    - "pre trained language model representations for language generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-2 (induced by source paper "depth adaptive graph recurrent network for text classification"):
  with members:
    - "graph based neural sentence ordering"
    - "depth adaptive transformer"
    - "learning to remember more with less memorization"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "cm net a novel collaborative memory network for spoken language understanding"
    - "universal transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "is attention all what you need an empirical investigation on convolution based active memory and self attention"):
  with members:
    - "augmenting self attention with persistent memory"
    - "pay less attention with lightweight and dynamic convolutions"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "modeling recurrence for transformer"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"

Connected through hyperedge HE-4 (induced by source paper "bivariate beta lstm"):
  with members:
    - "towards binary valued gates for robust lstm training"
    - "complex gated recurrent neural networks"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "boosting image captioning with attributes"
    - "recurrent batch normalization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "concare personalized clinical feature embedding via capturing the healthcare context"):
  with members:
    - "learning hierarchical representations of electronic health records for clinical outcome prediction"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "learning the joint representation of heterogeneous temporal events for clinical endpoint prediction"
    - "attention is all you need"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "gram graph based attention model for healthcare representation learning"
    - "wavenet a generative model for raw audio"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"

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)