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: "self attentional acoustic models"
  Abstract: Self-attention is a method of encoding sequences of vectors by relating these vectors to each-other based on pairwise similarities. These models have recently shown promising results for modeling discrete sequences, but they are non-trivial to apply to acoustic modeling due to computational and modeling issues. In this paper, we apply self-attention to acoustic modeling, proposing several improvements to mitigate these issues: First, self-attention memory grows quadratically in the sequence length, which we address through a downsampling technique. Second, we find that previous approaches to incorporate position information into the model are unsuitable and explore other representations and hybrid models to this end. Third, to stress the importance of local context in the acoustic signal, we propose a Gaussian biasing approach that allows explicit control over the context range. Experiments find that our model approaches a strong baseline based on LSTMs with network-in-network connections while being much faster to compute. Besides speed, we find that interpretability is a strength of self-attentional acoustic models, and demonstrate that self-attention heads learn a linguistically plausible division of labor.

Connected through hyperedge HE-1 (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-2 (induced by source paper "transformer based acoustic modeling for hybrid speech recognition"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "deja vu double feature presentation in deep transformer networks"
    - "a comparative study on transformer vs rnn in speech applications"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "state of the art speech recognition using multi stream self attention with dilated 1d convolutions"
    - "streaming end to end speech recognition for mobile devices"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "exploring phoneme level speech representations for end to end speech translation"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "optimizing segmentation granularity for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "low resource speech to text translation"
    - "xnmt the extensible neural machine translation toolkit"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "domain robust feature extraction for rapid low resource asr development"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"

Connected through hyperedge HE-4 (induced by source paper "gated group self attention for answer selection"):
  with members:
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "non local neural networks"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "deep semantic role labeling with self attention"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-5 (induced by source paper "transformers with convolutional context for asr"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "espnet end to end speech processing toolkit"
    - "the capio 2017 conversational speech recognition system"
    - "fully convolutional speech recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improved training of end to end attention models for speech recognition"
    - "optimal completion distillation for sequence learning"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "sequence to sequence singing synthesis using the feed forward transformer"):
  with members:
    - "singing voice synthesis using deep autoregressive neural networks for acoustic modeling"
    - "fastspeech fast robust and controllable text to speech"
    - "parallel neural text to speech"
    - "wgansing a multi voice singing voice synthesizer based on the wasserstein gan"
    - "adversarially trained end to end korean singing voice synthesis system"
    - "waveglow a flow based generative network for speech synthesis"
    - "neural speech synthesis with transformer network"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"

Connected through hyperedge HE-7 (induced by source paper "neural simile recognition with cyclic multitask learning and local attention"):
  with members:
    - "open domain targeted sentiment analysis via span based extraction and classification"
    - "variational recurrent neural machine translation"
    - "a unified model for opinion target extraction and target sentiment prediction"
    - "modeling localness for self attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "variational neural discourse relation recognizer"
    - "variational neural machine translation"
    - "recurrent neural network for text classification with multi task learning"

Connected through hyperedge HE-8 (induced by source paper "very deep self attention networks for end to end speech recognition"):
  with members:
    - "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "training deeper neural machine translation models with transparent attention"
    - "improved training of end to end attention models for speech recognition"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "very deep convolutional networks for end to end speech recognition"
    - "effective approaches to attention based 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)