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: "rnn approaches to text normalization a challenge"
  Abstract: This paper presents a challenge to the community: given a large corpus of written text aligned to its normalized spoken form, train an RNN to learn the correct normalization function. We present a data set of general text where the normalizations were generated using an existing text normalization component of a text-to-speech system. This data set will be released open-source in the near future. #R##N#We also present our own experiments with this data set with a variety of different RNN architectures. While some of the architectures do in fact produce very good results when measured in terms of overall accuracy, the errors that are produced are problematic, since they would convey completely the wrong message if such a system were deployed in a speech application. On the other hand, we show that a simple FST-based filter can mitigate those errors, and achieve a level of accuracy not achievable by the RNN alone. #R##N#Though our conclusions are largely negative on this point, we are actually not arguing that the text normalization problem is intractable using an pure RNN approach, merely that it is not going to be something that can be solved merely by having huge amounts of annotated text data and feeding that to a general RNN model. And when we open-source our data, we will be providing a novel data set for sequence-to-sequence modeling in the hopes that the the community can find better solutions. #R##N#The data used in this work have been released and are available at: this https URL

Connected through hyperedge HE-1 (induced by source paper "text normalization using memory augmented neural networks"):
  with members:
    - "one shot learning with memory augmented neural networks"
    - "xgboost a scalable tree boosting system"
    - "effective approaches to attention based neural machine translation"
    - "learning to transduce with unbounded memory"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "inferring algorithmic patterns with stack augmented recurrent nets"
    - "neural turing machines"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "a character level approach to the text normalization problem based on a new causal encoder"):
  with members:
    - "fully character level neural machine translation without explicit segmentation"
    - "wavenet a generative model for raw audio"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "a hybrid text normalization system using multi head self attention for mandarin"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "focal loss for dense object detection"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "who wrote this book a challenge for e commerce"):
  with members:
    - "merging datasets through deep learning"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "normalization of transliterated words in code mixed data using seq2seq model levenshtein distance"):
  with members:
    - "analyzing roles of classifiers and code mixed factors for sentiment identification"
    - "language identification of bengali english code mixed data using character phonetic based lstm models"
    - "automatic normalization of word variations in code mixed social media text"
    - "preparing bengali english code mixed corpus for sentiment analysis of indian languages"
    - "attention is all you need"
    - "deepnorm a deep learning approach to text normalization"
    - "shallow parsing pipeline for hindi english code mixed social media text"
    - "fully character level neural machine translation without explicit segmentation"

Connected through hyperedge HE-6 (induced by source paper "deep learning evaluation using deep linguistic processing"):
  with members:
    - "inferring and executing programs for visual reasoning"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "the promise of premise harnessing question premises in visual question answering"
    - "shapeworld a new test methodology for multimodal language understanding"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-7 (induced by source paper "shapeworld a new test methodology for multimodal language understanding"):
  with members:
    - "discovering objects and their relations from entangled scene representations"
    - "analyzing the behavior of visual question answering models"
    - "understanding deep learning requires rethinking generalization"
    - "learning to compose neural networks for question answering"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "hierarchical question image co attention for visual question answering"
    - "revisiting visual question answering baselines"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"

Connected through hyperedge HE-8 (induced by source paper "tacotron towards end to end speech synthesis"):
  with members:
    - "deep voice real time neural text to speech"
    - "samplernn an unconditional end to end neural audio generation model"
    - "fast compact and high quality lstm rnn based statistical parametric speech synthesizers for mobile devices"
    - "fully character level neural machine translation without explicit segmentation"
    - "wavenet a generative model for raw audio"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image 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.CL(Computation and Language)