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: "augmenting self attention with persistent memory"
  Abstract: Transformer networks have lead to important progress in language modeling and machine translation. These models include two consecutive modules, a feed-forward layer and a self-attention layer. The latter allows the network to capture long term dependencies and are often regarded as the key ingredient in the success of Transformers. Building upon this intuition, we propose a new model that solely consists of attention layers. More precisely, we augment the self-attention layers with persistent memory vectors that play a similar role as the feed-forward layer. Thanks to these vectors, we can remove the feed-forward layer without degrading the performance of a transformer. Our evaluation shows the benefits brought by our model on standard character and word level language modeling benchmarks.

Connected through hyperedge HE-1 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "reducing transformer depth on demand with structured dropout"
    - "compressive transformers for long range sequence modelling"
    - "pre trained language model representations for language generation"
    - "r transformer recurrent neural network enhanced transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-2 (induced by source paper "reformer the efficient transformer"):
  with members:
    - "stand alone self attention in vision models"
    - "low memory neural network training a technical report"
    - "generating long sequences with sparse transformers"
    - "large memory layers with product keys"
    - "adaptive attention span in transformers"
    - "generating wikipedia by summarizing long sequences"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "m 2 meshed memory transformer for image captioning"):
  with members:
    - "hierarchy parsing for image captioning"
    - "image captioning transforming objects into words"
    - "attention on attention for image captioning"
    - "reflective decoding network for image captioning"
    - "videobert a joint model for video and language representation learning"
    - "nocaps novel object captioning at scale"
    - "neural baby talk"
    - "show control and tell a framework for generating controllable and grounded captions"

Connected through hyperedge HE-4 (induced by source paper "smart training shallow memory aware transformers for robotic explainability"):
  with members:
    - "embodied vision and language navigation with dynamic convolutional filters"
    - "image captioning transforming objects into words"
    - "neural baby talk"
    - "probabilistic object detection definition and evaluation"
    - "on evaluation of embodied navigation agents"
    - "show control and tell a framework for generating controllable and grounded captions"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "muse parallel multi scale attention for sequence to sequence learning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "joint source target self attention with locality constraints"
    - "the evolved transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "depth growing for neural machine translation"
    - "fixup initialization residual learning without normalization"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-6 (induced by source paper "transformers without tears improving the normalization of self attention"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "root mean square layer normalization"
    - "on the variance of the adaptive learning rate and beyond"
    - "fixup initialization residual learning without normalization"
    - "massively multilingual neural machine translation"
    - "scaling neural machine translation"
    - "training tips for the transformer model"
    - "when and why are pre trained word embeddings useful for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "is attention all what you need an empirical investigation on convolution based active memory and self attention"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "r transformer recurrent neural network enhanced transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "modeling recurrence for transformer"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"

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)