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: "very deep self attention networks for end to end speech recognition"
  Abstract: Recently, end-to-end sequence-to-sequence models for speech recognition have gained significant interest in the research community. While previous architecture choices revolve around time-delay neural networks (TDNN) and long short-term memory (LSTM) recurrent neural networks, we propose to use self-attention via the Transformer architecture as an alternative. Our analysis shows that deep Transformer networks with high learning capacity are able to exceed performance from previous end-to-end approaches and even match the conventional hybrid systems. Moreover, we trained very deep models with up to 48 Transformer layers for both encoder and decoders combined with stochastic residual connections, which greatly improve generalizability and training efficiency. The resulting models outperform all previous end-to-end ASR approaches on the Switchboard benchmark. An ensemble of these models achieve 9.9% and 17.7% WER on Switchboard and CallHome test sets respectively. This finding brings our end-to-end models to competitive levels with previous hybrid systems. Further, with model ensembling the Transformers can outperform certain hybrid systems, which are more complicated in terms of both structure and training procedure.

Connected through hyperedge HE-1 (induced by source paper "improving uyghur asr systems with decoders using morpheme based language models"):
  with members:
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-2 (induced by source paper "reducing transformer depth on demand with structured dropout"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "adaptively sparse transformers"
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning deep transformer models for machine translation"
    - "pre trained language model representations for language generation"
    - "well read students learn better on the importance of pre training compact models"

Connected through hyperedge HE-3 (induced by source paper "flaubert unsupervised language model pre training for french"):
  with members:
    - "arabert transformer based model for arabic language understanding"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "reducing transformer depth on demand with structured dropout"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "why deep transformers are difficult to converge from computation order to lipschitz restricted parameter initialization"
    - "paws paraphrase adversaries from word scrambling"

Connected through hyperedge HE-4 (induced by source paper "improving deep transformer with depth scaled initialization and merged attention"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "generating long sequences with sparse transformers"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "blockwise parallel decoding for deep autoregressive models"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-5 (induced by source paper "efficient attention attention with linear complexities"):
  with members:
    - "connnet a long range relation aware pixel connectivity network for salient segmentation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "learning depth with convolutional spatial propagation network"
    - "yolov3 an incremental improvement"
    - "video based person re identification via 3d convolutional networks and non local attention"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "non local recurrent network for image restoration"
    - "large scale gan training for high fidelity natural image synthesis"

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)