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: "temporal attention model for neural machine translation"
  Abstract: Attention-based Neural Machine Translation (NMT) models suffer from attention deficiency issues as has been observed in recent research. We propose a novel mechanism to address some of these limitations and improve the NMT attention. Specifically, our approach memorizes the alignments temporally (within each sentence) and modulates the attention with the accumulated temporal memory, as the decoder generates the candidate translation. We compare our approach against the baseline NMT model and two other related approaches that address this issue either explicitly or implicitly. Large-scale experiments on two language pairs show that our approach achieves better and robust gains over the baseline and related NMT approaches. Our model further outperforms strong SMT baselines in some settings even without using ensembles.

Connected through hyperedge HE-1 (induced by source paper "controlling utterance length in nmt based word segmentation with attention"):
  with members:
    - "attention is not not explanation"
    - "attention is not explanation"
    - "tied multitask learning for neural speech translation"
    - "learning to segment inputs for nmt favors character level processing"
    - "low resource speech to text translation"
    - "on the alignment problem in multi head attention based neural machine translation"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-2 (induced by source paper "evaluating sequence to sequence models for handwritten text recognition"):
  with members:
    - "an efficient end to end neural model for handwritten text recognition"
    - "accurate data efficient unconstrained text recognition with convolutional neural networks"
    - "online and linear time attention by enforcing monotonic alignments"
    - "monotonic chunkwise attention"
    - "attention is all you need"
    - "label organized memory augmented neural network"
    - "convolutional sequence to sequence learning"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "quality of syntactic implication of rl based sentence summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "multi reward reinforced summarization with saliency and entailment"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "sentence simplification with deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "modeling source syntax for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "weakly labelled audioset tagging with attention neural networks"):
  with members:
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "attention based deep multiple instance learning"
    - "identify locate and separate audio visual object extraction in large video collections using weak supervision"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "audio set classification with attention model a probabilistic perspective"
    - "a structured self attentive sentence embedding"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "a joint detection classification model for audio tagging of weakly labelled data"

Connected through hyperedge HE-5 (induced by source paper "generating classical chinese poems from vernacular chinese"):
  with members:
    - "unsupervised clinical language translation"
    - "style transfer as unsupervised machine translation"
    - "unsupervised image captioning"
    - "how images inspire poems generating classical chinese poetry from images with memory networks"
    - "chinese poetry generation with a working memory model"
    - "attention is all you need"
    - "towards speech to text translation without speech recognition"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-6 (induced by source paper "multi stream network with temporal attention for environmental sound classification"):
  with members:
    - "learning environmental sounds with multi scale convolutional neural network"
    - "a simple model for detection of rare sound events"
    - "deep convolutional neural network with mixup for environmental sound classification"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"
    - "learning from between class examples for deep sound recognition"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-7 (induced by source paper "robust neural abstractive summarization systems and evaluation against adversarial information"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "attention is all you need"
    - "semi supervised multitask learning for sequence labeling"
    - "get to the point summarization with pointer generator networks"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "faithful to the original fact aware neural abstractive summarization"

Connected through hyperedge HE-8 (induced by source paper "an attention based approach for single image super resolution"):
  with members:
    - "salient object detection by lossless feature reflection"
    - "weakly supervised salient object detection using image labels"
    - "deep edge guided recurrent residual learning for image super resolution"
    - "attention is all you need"
    - "enhanced deep residual networks for single image super resolution"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "densely connected convolutional networks"

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)