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 neural network regularization"
  Abstract: We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include language modeling, speech recognition, image caption generation, and machine translation.

Connected through hyperedge HE-1 (induced by source paper "the implicit and explicit regularization effects of dropout"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "kernel and deep regimes in overparametrized models"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "transformer xl attentive language models beyond a fixed length context"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "implicit regularization in deep matrix factorization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"

Connected through hyperedge HE-2 (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-3 (induced by source paper "modality compensation network cross modal adaptation for action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "temporal bilinear networks for video action recognition"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "pku mmd a large scale benchmark for continuous multi modal human action understanding"

Connected through hyperedge HE-4 (induced by source paper "delving deeper into the decoder for video captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "a semantics assisted video captioning model trained with scheduled sampling"
    - "a hierarchical reinforced sequence operation method for unsupervised text style transfer"
    - "eco efficient convolutional network for online video understanding"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "reinforced video captioning with entailment rewards"
    - "attend and interact higher order object interactions for video understanding"

Connected through hyperedge HE-5 (induced by source paper "mixed precision deep learning based on computational memory"):
  with members:
    - "long short term memory networks in memristor crossbar arrays"
    - "neuromorphic computing with multi memristive synapses"
    - "mixed precision in memory computing"
    - "training lstm networks with resistive cross point devices"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "training and inference with integers in deep neural networks"
    - "an analysis of neural language modeling at multiple scales"
    - "an improved evaluation framework for generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "evaluating the progress of deep learning for visual relational concepts"):
  with members:
    - "v prom a benchmark for visual reasoning using visual progressive matrices"
    - "same different problems strain convolutional neural networks"
    - "a simple neural network module for relational reasoning"
    - "squeeze and excitation networks"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "iq of neural networks"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"

Connected through hyperedge HE-7 (induced by source paper "autofoley artificial synthesis of synchronized sound tracks for silent videos with deep learning"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "learning sight from sound ambient sound provides supervision for visual learning"
    - "multimodal machine learning a survey and taxonomy"
    - "fast slow recurrent neural networks"
    - "look listen and learn"
    - "aenet learning deep audio features for video analysis"
    - "video frame interpolation via adaptive separable convolution"
    - "visual to sound generating natural sound for videos in the wild"

Connected through hyperedge HE-8 (induced by source paper "fundamentals of recurrent neural network rnn and long short term memory lstm network"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "long short term memory as a dynamically computed element wise weighted sum"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "neural ordinary differential equations"
    - "recent advances in recurrent neural networks"
    - "nais net stable deep networks from non autonomous differential equations"
    - "lstm a search space odyssey"

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.NE(Neural and Evolutionary Computing)