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: "learning online alignments with continuous rewards policy gradient"
  Abstract: Sequence-to-sequence models with soft attention had significant success in machine translation, speech recognition, and question answering. Though capable and easy to use, they require that the entirety of the input sequence is available at the beginning of inference, an assumption that is not valid for instantaneous translation and speech recognition. To address this problem, we present a new method for solving sequence-to-sequence problems using hard online alignments instead of soft offline alignments. The online alignments model is able to start producing outputs without the need to first process the entire input sequence. A highly accurate online sequence-to-sequence model is useful because it can be used to build an accurate voice-based instantaneous translator. Our model uses hard binary stochastic decisions to select the timesteps at which outputs will be produced. The model is trained to produce these stochastic decisions using a standard policy gradient method. In our experiments, we show that this model achieves encouraging performance on TIMIT and Wall Street Journal (WSJ) speech recognition datasets.

Connected through hyperedge HE-1 (induced by source paper "incremental learning with maximum entropy regularization rethinking forgetting and intransigence"):
  with members:
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "deesil deep shallow incremental learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "end to end incremental learning"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-2 (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-3 (induced by source paper "the shmoop corpus a dataset of stories with loosely aligned summaries"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "get to the point summarization with pointer generator networks"
    - "the narrativeqa reading comprehension challenge"
    - "squad 100 000 questions for machine comprehension of text"
    - "movieqa understanding stories in movies through question answering"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"

Connected through hyperedge HE-4 (induced by source paper "deep lip reading a comparison of models and an online application"):
  with members:
    - "end to end audiovisual speech recognition"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "improved training for online end to end speech recognition systems"
    - "online and linear time attention by enforcing monotonic alignments"
    - "wav2letter an end to end convnet based speech recognition system"
    - "lipnet end to end sentence level lipreading"
    - "convolutional sequence to sequence learning"
    - "depthwise separable convolutions for neural machine translation"

Connected through hyperedge HE-5 (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-6 (induced by source paper "maximum entropy fine grained classification"):
  with members:
    - "generalized orderless pooling performs implicit salient matching"
    - "improved bilinear pooling with cnns"
    - "regularizing neural networks by penalizing confident output distributions"
    - "asynchronous methods for deep reinforcement learning"
    - "compact bilinear pooling"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "regularizing neural networks by penalizing confident output distributions"):
  with members:
    - "disturblabel regularizing cnn on the loss layer"
    - "deep networks with stochastic depth"
    - "deep recurrent models with fast forward connections for neural machine translation"
    - "densely connected convolutional networks"
    - "asynchronous methods for deep reinforcement learning"
    - "recurrent highway networks"
    - "using the output embedding to improve language models"
    - "fractalnet ultra deep neural networks without residuals"

Connected through hyperedge HE-8 (induced by source paper "an online sequence to sequence model for noisy speech recognition"):
  with members:
    - "variational inference for monte carlo objectives"
    - "a neural transducer"
    - "importance weighted autoencoders"
    - "end to end memory networks"
    - "listen attend and spell"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "gradient estimation using stochastic computation graphs"
    - "attention based models for speech recognition"

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)