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: "a neural transducer"
  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 or tasks that have long input sequences and output sequences. This is because they generate an output sequence conditioned on an entire input sequence. In this paper, we present a Neural Transducer that can make incremental predictions as more input arrives, without redoing the entire computation. Unlike sequence-to-sequence models, the Neural Transducer computes the next-step distribution conditioned on the partially observed input sequence and the partially generated sequence. At each time step, the transducer can decide to emit zero to many output symbols. The data can be processed using an encoder and presented as input to the transducer. The discrete decision to emit a symbol at every time step makes it difficult to learn with conventional backpropagation. It is however possible to train the transducer by using a dynamic programming algorithm to generate target discrete decisions. Our experiments show that the Neural Transducer works well in settings where it is required to produce output predictions as data come in. We also find that the Neural Transducer performs well for long sequences even when attention mechanisms are not used.

Connected through hyperedge HE-1 (induced by source paper "efficient content based sparse attention with routing transformers"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "adaptive attention span in transformers"
    - "generating wikipedia by summarizing long sequences"
    - "self attention with relative position representations"
    - "an analysis of neural language modeling at multiple scales"

Connected through hyperedge HE-2 (induced by source paper "recent advances in recurrent neural networks"):
  with members:
    - "gated orthogonal recurrent units on learning to forget"
    - "generalization of deep neural networks for chest pathology classification in x rays using generative adversarial networks"
    - "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"
    - "interpretation of mammogram and chest radiograph reports using deep neural networks preliminary results"
    - "recurrent dropout without memory loss"
    - "recurrent memory network for language modeling"
    - "wavenet a generative model for raw audio"
    - "pixel recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "online and linear time attention by enforcing monotonic alignments"):
  with members:
    - "online segment to segment neural transduction"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "adaptive computation time for recurrent neural networks"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "tensorflow a system for large scale machine learning"
    - "end to end memory networks"

Connected through hyperedge HE-4 (induced by source paper "an online sequence to sequence model for noisy speech recognition"):
  with members:
    - "learning online alignments with continuous rewards policy gradient"
    - "variational inference for monte carlo objectives"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "monotonic chunkwise attention"):
  with members:
    - "learning hard alignments with variational inference"
    - "get to the point summarization with pointer generator networks"
    - "tacotron towards end to end speech synthesis"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "online segment to segment neural transduction"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "very deep convolutional networks for end to end speech recognition"
    - "learning online alignments with continuous rewards policy gradient"

Connected through hyperedge HE-6 (induced by source paper "learning online alignments with continuous rewards policy gradient"):
  with members:
    - "end to end memory networks"
    - "listen attend and spell"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "gradient estimation using stochastic computation graphs"
    - "grid long short term memory"
    - "reinforcement learning neural turing machines revised"
    - "neural variational inference and learning in belief networks"
    - "neural turing machines"

Connected through hyperedge HE-7 (induced by source paper "morphological inflection generation with hard monotonic attention"):
  with members:
    - "online and linear time attention by enforcing monotonic alignments"
    - "online segment to segment neural transduction"
    - "single model encoder decoder with explicit morphological representation for reinflection"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "neural machine translation in linear time"
    - "factored neural machine translation"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "online segment to segment neural transduction"):
  with members:
    - "pointing the unknown words"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"
    - "learning to transduce with unbounded memory"
    - "segmental recurrent neural networks"
    - "ask me anything dynamic memory networks for natural language processing"
    - "adam a method for stochastic optimization"
    - "neural turing machines"

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)