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: "on multiplicative integration with recurrent neural networks"
  Abstract: We introduce a general and simple structural design called Multiplicative Integration (MI) to improve recurrent neural networks (RNNs). MI changes the way in which information from difference sources flows and is integrated in the computational building block of an RNN, while introducing almost no extra parameters. The new structure can be easily embedded into many popular RNN models, including LSTMs and GRUs. We empirically analyze its learning behaviour and conduct evaluations on several tasks using different RNN models. Our experimental results demonstrate that Multiplicative Integration can provide a substantial performance boost over many of the existing RNN models.

Connected through hyperedge HE-1 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"
    - "a flexible approach to automated rnn architecture generation"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"

Connected through hyperedge HE-2 (induced by source paper "adversarial video generation on complex datasets"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "dance dance generation motion transfer for internet videos"
    - "generating diverse high fidelity images with vq vae 2"
    - "videoflow a flow based generative model for video"
    - "generating long sequences with sparse transformers"
    - "scaling autoregressive video models"
    - "cgans with projection discriminator"
    - "video to video synthesis"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "character level recurrent neural networks in practice comparing training and sampling schemes"):
  with members:
    - "large scale user modeling with recurrent neural networks for music discovery on multiple time scales"
    - "lstm a search space odyssey"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "fast slow recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "improved recurrent neural networks for session based recommendations"
    - "multiplicative lstm for sequence modelling"

Connected through hyperedge HE-6 (induced by source paper "scene based factored attention for image captioning"):
  with members:
    - "boosted attention leveraging human attention for image captioning"
    - "learning to guide decoding for image captioning"
    - "discriminability objective for training descriptive captions"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "improved image captioning via policy gradient optimization of spider"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-7 (induced by source paper "stabilizing transformers for reinforcement learning"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "meta reinforcement learning as task inference"
    - "transformer xl attentive language models beyond a fixed length context"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"
    - "generating wikipedia by summarizing long sequences"

Connected through hyperedge HE-8 (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"

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)