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 highway networks"
  Abstract: Many sequential processing tasks require complex nonlinear transition functions from one step to the next. However, recurrent neural networks with 'deep' transition functions remain difficult to train, even when using Long Short-Term Memory (LSTM) networks. We introduce a novel theoretical analysis of recurrent networks based on Gersgorin's circle theorem that illuminates several modeling and optimization issues and improves our understanding of the LSTM cell. Based on this analysis we propose Recurrent Highway Networks, which extend the LSTM architecture to allow step-to-step transition depths larger than one. Several language modeling experiments demonstrate that the proposed architecture results in powerful and efficient models. On the Penn Treebank corpus, solely increasing the transition depth from 1 to 10 improves word-level perplexity from 90.6 to 65.4 using the same number of parameters. On the larger Wikipedia datasets for character prediction (text8 and enwik8), RHNs outperform all previous results and achieve an entropy of 1.27 bits per character.

Connected through hyperedge HE-1 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition"):
  with members:
    - "object part attention model for fine grained image classification"
    - "multi attention multi class constraint for fine grained image recognition"
    - "learning to navigate for fine grained classification"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "attend and rectify a gated attention mechanism for fine grained recovery"
    - "learn to pay attention"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "encoding based memory modules for recurrent neural networks"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "linear memory networks"
    - "lstm a search space odyssey"
    - "recent trends in deep learning based natural language processing"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "hierarchical multiscale recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "on architectures for including visual information in neural language models for image description"):
  with members:
    - "transfer learning from language models to image caption generators better models may not transfer better"
    - "visual entailment a novel task for fine grained image understanding"
    - "do better imagenet models transfer better"
    - "quantifying the amount of visual information used by neural caption generators"
    - "pre gen metrics predicting caption quality metrics without generating captions"
    - "object counts bringing explicit detections back into image captioning"
    - "the memad submission to the wmt18 multimodal translation task"
    - "grounded textual entailment"

Connected through hyperedge HE-4 (induced by source paper "deep independently recurrent neural network indrnn"):
  with members:
    - "temporal transformer networks joint learning of invariant and discriminative time warping"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "part based graph convolutional network for action recognition"
    - "learning long term dependencies via fourier recurrent units"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"

Connected through hyperedge HE-5 (induced by source paper "transformer xl attentive language models beyond a fixed length context"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "fast parametric learning with activation memorization"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "an analysis of neural language modeling at multiple scales"
    - "darts differentiable architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mesh tensorflow deep learning for supercomputers"

Connected through hyperedge HE-6 (induced by source paper "explicit sparse transformer concentrated attention through explicit selection"):
  with members:
    - "adaptively sparse transformers"
    - "muse parallel multi scale attention for sequence to sequence learning"
    - "pay less attention with lightweight and dynamic convolutions"
    - "sparse sequence to sequence models"
    - "joint source target self attention with locality constraints"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fixup initialization residual learning without normalization"
    - "universal transformers"

Connected through hyperedge HE-7 (induced by source paper "compressive transformers for long range sequence modelling"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "the curious case of neural text degeneration"
    - "dynamic evaluation of transformer language models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "large memory layers with product keys"

Connected through hyperedge HE-8 (induced by source paper "searching for a robust neural architecture in four gpu hours"):
  with members:
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "searching for efficient multi scale architectures for dense image prediction"

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)