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: "morphological inflection generation with hard monotonic attention"
  Abstract: We present a neural model for morphological inflection generation which employs a hard attention mechanism, inspired by the nearly-monotonic alignment commonly found between the characters in a word and the characters in its inflection. We evaluate the model on three previously studied morphological inflection generation datasets and show that it provides state of the art results in various setups compared to previous neural and non-neural approaches. Finally we present an analysis of the continuous representations learned by both the hard and soft attention \cite{bahdanauCB14} models for the task, shedding some light on the features such models extract.

Connected through hyperedge HE-1 (induced by source paper "parallel iterative edit models for local sequence transduction"):
  with members:
    - "corpora generation for grammatical error correction"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "neural grammatical error correction with finite state transducers"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "learning to represent edits"

Connected through hyperedge HE-2 (induced by source paper "towards realistic practices in low resource natural language processing the development set"):
  with members:
    - "imitation learning for neural morphological string transduction"
    - "bootstrapping transliteration with constrained discovery for low resource languages"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "diverse few shot text classification with multiple metrics"
    - "one shot neural cross lingual transfer for paradigm completion"

Connected through hyperedge HE-3 (induced by source paper "pushing the limits of low resource morphological inflection"):
  with members:
    - "automatic keyboard layout design for low resource latin script languages"
    - "exact hard monotonic attention for character level transduction"
    - "rapid adaptation of neural machine translation to new languages"
    - "leveraging translations for speech transcription in low resource settings"
    - "unsupervised machine translation using monolingual corpora only"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "attention is all you need"
    - "conll sigmorphon 2017 shared task universal morphological reinflection in 52 languages"

Connected through hyperedge HE-4 (induced by source paper "sparse sequence to sequence models"):
  with members:
    - "attention is not explanation"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "sparse and constrained attention for neural machine translation"
    - "hard non monotonic attention for character level transduction"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "classical structured prediction losses for sequence to sequence learning"
    - "massively multilingual neural grapheme to phoneme conversion"

Connected through hyperedge HE-5 (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"
    - "online and linear time attention by enforcing monotonic alignments"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "implicit dimension identification in user generated text with lstm networks"):
  with members:
    - "recurrent ladder networks"
    - "deep learning for hate speech detection in tweets"
    - "fake news detection on social media a data mining perspective"
    - "w2vlda almost unsupervised system for aspect based sentiment analysis"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "pointer sentinel mixture models"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-7 (induced by source paper "exact hard monotonic attention for character level transduction"):
  with members:
    - "imitation learning for neural morphological string transduction"
    - "hard non monotonic attention for character level transduction"
    - "align and copy uzh at sigmorphon 2017 shared task for morphological reinflection"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "online segment to segment neural transduction"
    - "sequence to sequence neural network models for transliteration"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "improved training of end to end attention models for speech recognition"):
  with members:
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "online and linear time attention by enforcing monotonic alignments"
    - "monotonic chunkwise attention"
    - "letter based speech recognition with gated convnets"
    - "state of the art speech recognition with sequence to sequence models"
    - "improving end to end speech recognition with policy learning"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"

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)