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: "efficient attention using a fixed size memory representation"
  Abstract: The standard content-based attention mechanism typically used in sequence-to-sequence models is computationally expensive as it requires the comparison of large encoder and decoder states at each time step. In this work, we propose an alternative attention mechanism based on a fixed size memory representation that is more efficient. Our technique predicts a compact set of K attention contexts during encoding and lets the decoder compute an efficient lookup that does not need to consult the memory. We show that our approach performs on-par with the standard attention mechanism while yielding inference speedups of 20% for real-world translation tasks and more for tasks with longer sequences. By visualizing attention scores we demonstrate that our models learn distinct, meaningful alignments.

Connected through hyperedge HE-1 (induced by source paper "generating long sequences with sparse transformers"):
  with members:
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "generating wikipedia by summarizing long sequences"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "character level language modeling with deeper self attention"
    - "music transformer"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "monotonic chunkwise attention"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "scram spatially coherent randomized attention maps"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "generative image inpainting with contextual attention"
    - "non local neural networks"
    - "monotonic chunkwise attention"
    - "attention is all you need"
    - "visual attribute transfer through deep image analogy"

Connected through hyperedge HE-3 (induced by source paper "object based multi temporal and multi source land cover mapping leveraging hierarchical class relationships"):
  with members:
    - "temporal convolutional neural network for the classification of satellite image time series"
    - "fine grained attention mechanism for neural machine translation"
    - "duplo a dual view point deep learning architecture for time series classification"
    - "attention is all you need"
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "why should i trust you explaining the predictions of any classifier"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "supervised level wise pretraining for recurrent neural network initialization in multi class classification"):
  with members:
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "duplo a dual view point deep learning architecture for time series classification"
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "duplo a dual view point deep learning architecture for time series classification"):
  with members:
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "mrfusion a deep learning architecture to fuse pan and ms imagery for land cover mapping"
    - "multi temporal land cover classification with sequential recurrent encoders"
    - "two stream rnn cnn for action recognition in 3d videos"
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "conditional image generation with pixelcnn decoders"

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.CL(Computation and Language)