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: "dynamic evaluation of neural sequence models"
  Abstract: We present methodology for using dynamic evaluation to improve neural sequence models. Models are adapted to recent history via a gradient descent based mechanism, causing them to assign higher probabilities to re-occurring sequential patterns. Dynamic evaluation outperforms existing adaptation approaches in our comparisons. Dynamic evaluation improves the state-of-the-art word-level perplexities on the Penn Treebank and WikiText-2 datasets to 51.1 and 44.3 respectively, and the state-of-the-art character-level cross-entropies on the text8 and Hutter Prize datasets to 1.19 bits/char and 1.08 bits/char respectively.

Connected through hyperedge HE-1 (induced by source paper "maxup a simple way to improve generalization of neural network training"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "improving neural language modeling via adversarial training"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial examples improve image recognition"
    - "freelb enhanced adversarial training for language understanding"
    - "fixing the train test resolution discrepancy"

Connected through hyperedge HE-2 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training"):
  with members:
    - "putting machine translation in context with the noisy channel model"
    - "microsoft translator at wmt 2019 towards large scale document level neural machine translation"
    - "context aware monolingual repair for neural machine translation"
    - "when a good translation is wrong in context context aware machine translation improves on deixis ellipsis and lexical cohesion"
    - "revisiting self training for neural sequence generation"
    - "fill in the blanks imputing missing sentences for larger context neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "modeling coherence for discourse neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "transformer on a diet"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "albert a lite bert for self supervised learning of language representations"
    - "language models with transformers"
    - "bp transformer modelling long range context via binary partitioning"
    - "star transformer"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

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 "improving neural language modeling via adversarial training"):
  with members:
    - "partially shuffling the training data to improve language models"
    - "representation degeneration problem in training natural language generation models"
    - "fast parametric learning with activation memorization"
    - "learning towards minimum hyperspherical energy"
    - "frage frequency agnostic word representation"
    - "a la carte embedding cheap but effective induction of semantic feature vectors"
    - "an analysis of neural language modeling at multiple scales"
    - "darts differentiable architecture search"

Connected through hyperedge HE-6 (induced by source paper "graph guided architecture search for real time semantic segmentation"):
  with members:
    - "customizable architecture search for semantic segmentation"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "reinforced evolutionary neural architecture search"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-7 (induced by source paper "character n gram embeddings to improve rnn language models"):
  with members:
    - "an analysis of neural language modeling at multiple scales"
    - "selective encoding for abstractive sentence summarization"
    - "composing distributed representations of relational patterns"
    - "input to output gate to improve rnn language models"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "the mechanism of additive composition"

Connected through hyperedge HE-8 (induced by source paper "large scale answerer in questioner s mind for visual dialog question generation"):
  with members:
    - "multimodal hierarchical reinforcement learning policy for task oriented visual dialog"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "sliced recurrent neural networks"
    - "mine mutual information neural estimation"
    - "nsml meet the mlaas platform with a real world case study"

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)