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: "recurrent stacking of layers for compact neural machine translation models"
  Abstract: In neural machine translation (NMT), the most common practice is to stack a number of recurrent or feed-forward layers in the encoder and the decoder. As a result, the addition of each new layer improves the translation quality significantly. However, this also leads to a significant increase in the number of parameters. In this paper, we propose to share parameters across all the layers thereby leading to a recurrently stacked NMT model. We empirically show that the translation quality of a model that recurrently stacks a single layer 6 times is comparable to the translation quality of a model that stacks 6 separate layers. We also show that using pseudo-parallel corpora by back-translation leads to further significant improvements in translation quality.

Connected through hyperedge HE-1 (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"
    - "trellis networks for sequence modeling"
    - "relational recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "balancing cost and benefit with tied multi transformers"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "scaling neural machine translation"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"
    - "accelerating neural transformer via an average attention network"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "improving context aware neural machine translation with target side context"):
  with members:
    - "improving the transformer translation model with document level context"
    - "context aware neural machine translation learns anaphora resolution"
    - "a large scale test set for the evaluation of context aware pronoun translation in neural machine translation"
    - "neural machine translation with extended context"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "evaluating discourse phenomena in neural machine translation"
    - "exploiting cross sentence context for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "sharing attention weights for fast transformer"):
  with members:
    - "accelerating neural transformer via an average attention network"
    - "pieces of eight 8 bit neural machine translation"
    - "attention is all you need"
    - "mixed precision training"
    - "massive exploration of neural machine translation architectures"
    - "convolutional sequence to sequence learning"
    - "sequence level knowledge distillation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

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)