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: "recurrently controlled recurrent networks"
  Abstract: Recurrent neural networks (RNNs) such as long short-term memory and gated recurrent units are pivotal building blocks across a broad spectrum of sequence modeling problems. This paper proposes a recurrently controlled recurrent network (RCRN) for expressive and powerful sequence encoding. More concretely, the key idea behind our approach is to learn the recurrent gating functions using recurrent networks. Our architecture is split into two components - a controller cell and a listener cell whereby the recurrent controller actively influences the compositionality of the listener cell. We conduct extensive experiments on a myriad of tasks in the NLP domain such as sentiment analysis (SST, IMDb, Amazon reviews, etc.), question classification (TREC), entailment classification (SNLI, SciTail), answer selection (WikiQA, TrecQA) and reading comprehension (NarrativeQA). Across all 26 datasets, our results demonstrate that RCRN not only consistently outperforms BiLSTMs but also stacked BiLSTMs, suggesting that our controller architecture might be a suitable replacement for the widely adopted stacked architecture.

Connected through hyperedge HE-1 (induced by source paper "depth adaptive graph recurrent network for text classification"):
  with members:
    - "graph based neural sentence ordering"
    - "depth adaptive transformer"
    - "r transformer recurrent neural network enhanced 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"

Connected through hyperedge HE-2 (induced by source paper "a dynamically controlled recurrent neural network for modeling dynamical systems"):
  with members:
    - "the unreasonable effectiveness of the forget gate"
    - "adaptive skip intervals temporal abstraction for recurrent dynamical models"
    - "neural ordinary differential equations"
    - "lstm a search space odyssey"
    - "state of the art speech recognition with sequence to sequence models"
    - "dilated recurrent neural networks"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "recurrent batch normalization"

Connected through hyperedge HE-3 (induced by source paper "modeling multi action policy for task oriented dialogues"):
  with members:
    - "user modeling for task oriented dialogues"
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "latent intention dialogue models"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "end to end lstm based dialog control optimized with supervised and reinforcement learning"
    - "adam a method for stochastic optimization"

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.CL(Computation and Language)