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 transformer language models"
  Abstract: This research note combines two methods that have recently improved the state of the art in language modeling: Transformers and dynamic evaluation. Transformers use stacked layers of self-attention that allow them to capture long range dependencies in sequential data. Dynamic evaluation fits models to the recent sequence history, allowing them to assign higher probabilities to re-occurring sequential patterns. By applying dynamic evaluation to Transformer-XL models, we improve the state of the art on enwik8 from 0.99 to 0.94 bits/char, text8 from 1.08 to 1.04 bits/char, and WikiText-103 from 18.3 to 16.4 perplexity points.

Connected through hyperedge HE-1 (induced by source paper "sentencemim a latent variable language model"):
  with members:
    - "implicit deep latent variable models for text generation"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "preventing posterior collapse with delta vaes"
    - "a stable variational autoencoder for text modelling"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "improved variational autoencoders for text modeling using dilated convolutions"

Connected through hyperedge HE-2 (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"
    - "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"
    - "adaptive attention span in transformers"

Connected through hyperedge HE-3 (induced by source paper "megatron lm training multi billion parameter language models using model parallelism"):
  with members:
    - "generalization through memorization nearest neighbor language models"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "spanbert improving pre training by representing and predicting spans"
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-4 (induced by source paper "a simple recurrent unit with reduced tensor product representations"):
  with members:
    - "correlating neural and symbolic representations of language"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "frage frequency agnostic word representation"
    - "deep contextualized word representations"
    - "can neural networks understand logical entailment"
    - "learning to reason with third order tensor products"
    - "glomo unsupervisedly learned relational graphs as transferable representations"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-5 (induced by source paper "generalization through memorization nearest neighbor language models"):
  with members:
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "improving neural language models by segmenting attending and predicting the future"
    - "defending against neural fake news"
    - "transformer xl attentive language models beyond a fixed length context"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "retrieval augmented convolutional neural networks for improved robustness against adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "just add functions a neural symbolic language model"):
  with members:
    - "numeracy for language models evaluating and improving their ability to predict numbers"
    - "deep learning a critical appraisal"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "regularizing and optimizing lstm language models"
    - "a neural knowledge language model"
    - "numerically grounded language models for semantic error correction"
    - "improving neural language models with a continuous cache"

Connected through hyperedge HE-7 (induced by source paper "simplebooks long term dependency book dataset with simplified english vocabulary for word level language modeling"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"
    - "frage frequency agnostic word representation"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "regularizing and optimizing lstm language models"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "pointer sentinel mixture models"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "behavior gated language models"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "partially shuffling the training data to improve language models"
    - "trellis networks for sequence modeling"
    - "topic compositional neural language model"
    - "regularizing and optimizing lstm language models"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "recurrent neural network regularization"

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)