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: "accelerating neural transformer via an average attention network"
  Abstract: With parallelizable attention networks, the neural Transformer is very fast to train. However, due to the auto-regressive architecture and self-attention in the decoder, the decoding procedure becomes slow. To alleviate this issue, we propose an average attention network as an alternative to the self-attention network in the decoder of the neural Transformer. The average attention network consists of two layers, with an average layer that models dependencies on previous positions and a gating layer that is stacked over the average layer to enhance the expressiveness of the proposed attention network. We apply this network on the decoder part of the neural Transformer to replace the original target-side self-attention model. With masking tricks and dynamic programming, our model enables the neural Transformer to decode sentences over four times faster than its original version with almost no loss in training time and translation performance. We conduct a series of experiments on WMT17 translation tasks, where on 6 different language pairs, we obtain robust and consistent speed-ups in decoding.

Connected through hyperedge HE-1 (induced by source paper "faster transformer decoding n gram masked self attention"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "fast transformer decoding one write head is all you need"
    - "image transformer"
    - "generating wikipedia by summarizing long sequences"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "structure invariant testing for machine translation"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "machine learning testing survey landscapes and horizons"
    - "defensive quantization when efficiency meets robustness"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "achieving human parity on automatic chinese to english news translation"
    - "code2vec learning distributed representations of code"
    - "on adversarial examples for character level neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "context aware self attention networks"):
  with members:
    - "neural machine translation with adequacy oriented learning"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-4 (induced by source paper "automatically extracting challenge sets for non local phenomena neural machine translation"):
  with members:
    - "star transformer"
    - "linguistic evaluation of german english machine translation using a test suite"
    - "linguistic knowledge and transferability of contextual representations"
    - "semantic neural machine translation using amr"
    - "semantic structural evaluation for text simplification"
    - "do rnns learn human like abstract word order preferences"
    - "a challenge set for french english machine translation"
    - "automatic metric validation for grammatical error correction"

Connected through hyperedge HE-5 (induced by source paper "a lightweight recurrent network for sequence modeling"):
  with members:
    - "rational recurrences"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "simple recurrent units for highly parallelizable recurrence"
    - "deriving neural architectures from sequence and graph kernels"
    - "recurrent additive networks"
    - "breaking the softmax bottleneck a high rank rnn language model"

Connected through hyperedge HE-6 (induced by source paper "improving deep transformer with depth scaled initialization and merged attention"):
  with members:
    - "very deep self attention networks for end to end speech recognition"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "retrieving sequential information for non autoregressive neural machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "you may not need attention"
    - "a study of reinforcement learning for neural machine translation"
    - "fast decoding in sequence models using discrete latent variables"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "bag of words as target for neural machine translation"
    - "speeding up neural machine translation decoding by cube pruning"

Connected through hyperedge HE-8 (induced by source paper "sequence generation from both sides to the middle"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "selective encoding for abstractive sentence summarization"
    - "attention is all you need"
    - "sharp models on dull hardware fast and accurate neural machine translation decoding on the cpu"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "non autoregressive neural machine translation"

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)