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: "online and linear time attention by enforcing monotonic alignments"
  Abstract: Recurrent neural network models with an attention mechanism have proven to be extremely effective on a wide variety of sequence-to-sequence problems. However, the fact that soft attention mechanisms perform a pass over the entire input sequence when producing each element in the output sequence precludes their use in online settings and results in a quadratic time complexity. Based on the insight that the alignment between input and output sequence elements is monotonic in many problems of interest, we propose an end-to-end differentiable method for learning monotonic alignments which, at test time, enables computing attention online and in linear time. We validate our approach on sentence summarization, machine translation, and online speech recognition problems and achieve results competitive with existing sequence-to-sequence models.

Connected through hyperedge HE-1 (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"
    - "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"
    - "cold fusion training seq2seq models together with language models"

Connected through hyperedge HE-2 (induced by source paper "understanding and improving layer normalization"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformer xl attentive language models beyond a fixed length context"
    - "group normalization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "understanding batch normalization"
    - "character level language modeling with deeper self attention"
    - "self normalizing neural networks"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "rl lim reinforcement learning based locally interpretable modeling"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "regularizing black box models for improved interpretability"
    - "model agnostic supervised local explanations"
    - "explaining explanations an overview of interpretability of machine learning"
    - "learning to reweight examples for robust deep learning"
    - "on the robustness of interpretability methods"
    - "interpretable explorable approximations of black box models"
    - "sentence simplification with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "monotonic multihead attention"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "simpler and faster learning of adaptive policies for simultaneous translation"
    - "stacl simultaneous translation with implicit anticipation and controllable latency using prefix to prefix framework"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "memory augmented neural networks for machine translation"):
  with members:
    - "implementing neural turing machines"
    - "attention is all you need"
    - "memory enhanced decoder for neural machine translation"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "edinburgh neural machine translation systems for wmt 16"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "end to end memory networks"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "robust sequence to sequence acoustic modeling with stepwise monotonic attention for neural tts"):
  with members:
    - "forward attention in sequence to sequence acoustic modeling for speech synthesis"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "tacotron towards end to end speech synthesis"
    - "local monotonic attention mechanism for end to end speech and language processing"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "wavenet a generative model for raw audio"
    - "morphological inflection generation with hard monotonic attention"

Connected through hyperedge HE-7 (induced by source paper "simple unsupervised summarization by contextual matching"):
  with members:
    - "unsupervised sentence compression using denoising auto encoders"
    - "unsupervised abstractive sentence summarization using length controlled variational autoencoder"
    - "deep contextualized word representations"
    - "learning to encode text as human readable summaries using generative adversarial networks"
    - "a deep reinforced model for abstractive summarization"
    - "online segment to segment neural transduction"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "abstractive text summarization using sequence to sequence rnns and beyond"

Connected through hyperedge HE-8 (induced by source paper "teacher student training for robust tacotron based tts"):
  with members:
    - "robust sequence to sequence acoustic modeling with stepwise monotonic attention for neural tts"
    - "fastspeech fast robust and controllable text to speech"
    - "a new gan based end to end tts training algorithm"
    - "generalization in generation a closer look at exposure bias"
    - "robust and fine grained prosody control of end to end speech synthesis"
    - "semi supervised training for improving data efficiency in end to end speech synthesis"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "wavenet a generative model for raw audio"

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)