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 segment to segment neural transduction"
  Abstract: We introduce an online neural sequence to sequence model that learns to alternate between encoding and decoding segments of the input as it is read. By independently tracking the encoding and decoding representations our algorithm permits exact polynomial marginalization of the latent segmentation during training, and during decoding beam search is employed to find the best alignment path together with the predicted output sequence. Our model tackles the bottleneck of vanilla encoder-decoders that have to read and memorize the entire input sequence in their fixed-length hidden states before producing any output. It is different from previous attentive models in that, instead of treating the attention weights as output of a deterministic function, our model assigns attention weights to a sequential latent variable which can be marginalized out and permits online generation. Experiments on abstractive sentence summarization and morphological inflection show significant performance gains over the baseline encoder-decoders.

Connected through hyperedge HE-1 (induced by source paper "learning semantic parsers from denotations with latent structured alignments and abstract programs"):
  with members:
    - "learning to infer program sketches"
    - "learning to generalize from sparse and underspecified rewards"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "semantic parsing with syntax and table aware sql generation"
    - "allennlp a deep semantic natural language processing platform"
    - "decoupling structure and lexicon for zero shot semantic parsing"

Connected through hyperedge HE-2 (induced by source paper "multimodal transformer for unaligned multimodal language sequences"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-3 (induced by source paper "improving generalization of transformer for speech recognition with parallel schedule sampling and relative positional embedding"):
  with members:
    - "parallel scheduled sampling"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "self attention with relative position representations"
    - "an online attention based model for speech recognition"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "extending recurrent neural aligner for streaming end to end speech recognition in mandarin"
    - "attention is all you need"

Connected through hyperedge HE-4 (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"
    - "online and linear time attention by enforcing monotonic alignments"
    - "a deep reinforced model for abstractive summarization"
    - "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-5 (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"
    - "sequence to sequence neural network models for transliteration"
    - "morphological inflection generation with hard monotonic attention"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "tackling sequence to sequence mapping problems with neural networks"):
  with members:
    - "online and linear time attention by enforcing monotonic alignments"
    - "language as a latent variable discrete generative models for sentence compression"
    - "learning to compose words into sentences with reinforcement learning"
    - "improving neural machine translation models with monolingual data"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "transition based dependency parsing with stack long short term memory"

Connected through hyperedge HE-7 (induced by source paper "an operation sequence model for explainable neural machine translation"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "training tips for the transformer model"
    - "neural machine translation decoding with terminology constraints"
    - "qcri machine translation systems for iwslt 16"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"

Connected through hyperedge HE-8 (induced by source paper "learning neural templates for text generation"):
  with members:
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "adversarially regularized autoencoders"
    - "learning to generate one sentence biographies from wikidata"
    - "neural language modeling by jointly learning syntax and lexicon"
    - "challenges in data to document generation"

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)