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: "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
  Abstract: For most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine translation. Given a new sequence modeling task or dataset, which architecture should one use? We conduct a systematic evaluation of generic convolutional and recurrent architectures for sequence modeling. The models are evaluated across a broad range of standard tasks that are commonly used to benchmark recurrent networks. Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory. We conclude that the common association between sequence modeling and recurrent networks should be reconsidered, and convolutional networks should be regarded as a natural starting point for sequence modeling tasks. To assist related work, we have made code available at this http URL .

Connected through hyperedge HE-1 (induced by source paper "blank language models"):
  with members:
    - "tigs an inference algorithm for text infilling with gradient search"
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "synchronous bidirectional neural machine translation"
    - "restoring ancient text using deep learning a case study on greek epigraphy"
    - "mask predict parallel decoding of conditional masked language models"
    - "ctrl a conditional transformer language model for controllable generation"
    - "transformer xl attentive language models beyond a fixed length context"

Connected through hyperedge HE-2 (induced by source paper "adabert task adaptive bert compression with differentiable neural architecture search"):
  with members:
    - "q8bert quantized 8bit bert"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "extreme language model compression with optimal subwords and shared projections"
    - "bert rediscovers the classical nlp pipeline"
    - "q bert hessian based ultra low precision quantization of bert"
    - "structured pruning of large language models"

Connected through hyperedge HE-3 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "learning to drive in a day"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "lstm a search space odyssey"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-4 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition"):
  with members:
    - "q8bert quantized 8bit bert"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "a comparative study on transformer vs rnn in speech applications"
    - "language modeling with deep transformers"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "fully quantized transformer for improved translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-5 (induced by source paper "temporally folded convolutional neural networks for sequence forecasting"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "stcn stochastic temporal convolutional networks"
    - "recurrent neural networks for time series forecasting current status and future directions"
    - "recurrent convolutions for causal 3d cnns"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "lstm a search space odyssey"
    - "convolutional image captioning"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "transformer networks for trajectory forecasting"):
  with members:
    - "trajectory forecasts in unknown environments conditioned on grid based plans"
    - "trajectron multi agent generative trajectory forecasting with heterogeneous data for control"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "human motion trajectory prediction a survey"
    - "safecritic collision aware trajectory prediction"
    - "scene compliant trajectory forecast with agent centric spatio temporal grids"
    - "generating long sequences with sparse transformers"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"

Connected through hyperedge HE-7 (induced by source paper "forecastnet a time variant deep feed forward neural network architecture for multi step ahead time series forecasting"):
  with members:
    - "deep transformer models for time series forecasting the influenza prevalence case"
    - "recurrent neural networks for time series forecasting current status and future directions"
    - "enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting"
    - "foundations of sequence to sequence modeling for time series"
    - "attention is all you need"
    - "autoregressive convolutional neural networks for asynchronous time series"
    - "wavenet a generative model for raw audio"
    - "deep learning in neural networks"

Connected through hyperedge HE-8 (induced by source paper "graphtcn spatio temporal interaction modeling for human trajectory prediction"):
  with members:
    - "analyzing the variety loss in the context of probabilistic trajectory prediction"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "peeking into the future predicting future person activities and locations in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "convolutional neural network for trajectory prediction"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "social attention modeling attention in human crowds"

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)