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: "fast parametric learning with activation memorization"
  Abstract: Neural networks trained with backpropagation often struggle to identify classes that have been observed a small number of times. In applications where most class labels are rare, such as language modelling, this can become a performance bottleneck. One potential remedy is to augment the network with a fast-learning non-parametric model which stores recent activations and class labels into an external memory. We explore a simplified architecture where we treat a subset of the model parameters as fast memory stores. This can help retain information over longer time intervals than a traditional memory, and does not require additional space or compute. In the case of image classification, we display faster binding of novel classes on an Omniglot image curriculum task. We also show improved performance for word-based language models on news reports (GigaWord), books (Project Gutenberg) and Wikipedia articles (WikiText-103) --- the latter achieving a state-of-the-art perplexity of 29.2.

Connected through hyperedge HE-1 (induced by source paper "time aware large kernel convolutions"):
  with members:
    - "accelerating large kernel convolution using summed area tables"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "massively multilingual neural machine translation"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "scaling neural machine translation"

Connected through hyperedge HE-2 (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"
    - "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"
    - "learning longer term dependencies in rnns with auxiliary losses"

Connected through hyperedge HE-3 (induced by source paper "fairseq a fast extensible toolkit for sequence modeling"):
  with members:
    - "strategies for structuring story generation"
    - "mixture models for diverse machine translation tricks of the trade"
    - "weighted transformer network for machine translation"
    - "a stable and effective learning strategy for trainable greedy decoding"
    - "self attention with relative position representations"
    - "bottom up abstractive summarization"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "scaling neural machine translation"

Connected through hyperedge HE-4 (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-5 (induced by source paper "improving the gating mechanism of recurrent neural networks"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "transformer xl attentive language models beyond a fixed length context"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "towards binary valued gates for robust lstm training"
    - "optimizing agent behavior over long time scales by transporting value"
    - "trellis networks for sequence modeling"
    - "can recurrent neural networks warp time"

Connected through hyperedge HE-6 (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"
    - "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"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-7 (induced by source paper "augmenting self attention with persistent memory"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "adaptive attention span in transformers"
    - "self attention with relative position representations"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"

Connected through hyperedge HE-8 (induced by source paper "improving neural language models by segmenting attending and predicting the future"):
  with members:
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "an analysis of neural language modeling at multiple scales"
    - "darts differentiable architecture search"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "efficient neural architecture search via parameter sharing"
    - "adaptive input representations for neural language modeling"
    - "breaking the softmax bottleneck a high rank rnn language model"

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)