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: "an online sequence to sequence model using partial conditioning"
  Abstract: Sequence-to-sequence models have achieved impressive results on various tasks. However, they are unsuitable for tasks that require incremental predictions to be made as more data arrives. This is because they generate an output sequence conditioned on an entire input sequence. In this paper, we present a new model that can make incremental predictions as more input arrives, without redoing the entire computation. Unlike sequence-to-sequence models, our method computes the next-step distribution conditioned on the partial input sequence observed and the partial sequence generated. It accomplishes this goal using an encoder recurrent neural network (RNN) that computes features at the same frame rate as the input, and a transducer RNN that operates over blocks of input steps. The transducer RNN extends the sequence produced so far using a local sequence-to-sequence model. During training, our method uses alignment information to generate supervised targets for each block. Approximate alignment is easily available for tasks such as speech recognition, action recognition in videos, etc. During inference (decoding), beam search is used to find the most likely output sequence for an input sequence. This decoding is performed online - at the end of each block, the best candidates from the previous block are extended through the local sequence-to-sequence model. On TIMIT, our online method achieves 19.8% phone error rate (PER). For comparison with published sequence-to-sequence methods, we used a bidirectional encoder and achieved 18.7% PER compared to 17.6% from the best reported sequence-to-sequence model. Importantly, unlike sequence-to-sequence our model is minimally impacted by the length of the input. On artificially created longer utterances, it achieves 20.9% with a unidirectional model, compared to 20% from the best bidirectional sequence-to-sequence models.

Connected through hyperedge HE-1 (induced by source paper "optimizing speech recognition for the edge"):
  with members:
    - "stabilizing the lottery ticket hypothesis"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "streaming end to end speech recognition for mobile devices"
    - "toward domain invariant speech recognition via large scale training"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "the unreasonable effectiveness of the forget gate"
    - "snip single shot network pruning based on connection sensitivity"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-2 (induced by source paper "speculative beam search for simultaneous translation"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "simpler and faster learning of adaptive policies for simultaneous translation"
    - "learning to stop in structured prediction for neural machine translation"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "correcting length bias in neural machine translation"
    - "when to finish optimal beam search for neural text generation modulo beam size"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "assessing incrementality in sequence to sequence models"):
  with members:
    - "deep text to speech system with seq2seq model"
    - "transcoding compositionally using attention to find more generalizable solutions"
    - "on the convergence of adam and beyond"
    - "learning compositionally through attentive guidance"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "closed book training to improve summarization encoder memory"

Connected through hyperedge HE-4 (induced by source paper "simultaneous translation with flexible policy via restricted imitation learning"):
  with members:
    - "learning to stop in structured prediction for neural machine translation"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "you may not need attention"
    - "multi reference training with pseudo references for neural translation and text generation"
    - "stacl simultaneous translation with integrated anticipation and controllable latency"
    - "when to finish optimal beam search for neural text generation modulo beam size"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "an online attention based model for speech recognition"):
  with members:
    - "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"
    - "online and linear time attention by enforcing monotonic alignments"
    - "monotonic chunkwise attention"
    - "learning hard alignments with variational inference"
    - "exploring neural transducers for end to end speech recognition"
    - "improving the performance of online neural transducer models"
    - "local monotonic attention mechanism for end to end speech and language processing"

Connected through hyperedge HE-7 (induced by source paper "extending recurrent neural aligner for streaming end to end speech recognition in mandarin"):
  with members:
    - "online and linear time attention by enforcing monotonic alignments"
    - "state of the art speech recognition with sequence to sequence models"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "regularizing neural networks by penalizing confident output distributions"
    - "cold fusion training seq2seq models together with language models"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "very deep convolutional networks for end to end speech recognition"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "attention based sequence to sequence model for speech recognition development of state of the art system on librispeech and its application to non native english"):
  with members:
    - "improved training of end to end attention models for speech recognition"
    - "efficient softmax approximation for gpus"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "neural machine translation of rare words with subword units"
    - "attention based models for speech recognition"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"

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)