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: "segmental recurrent neural networks for end to end speech recognition"
  Abstract: We study the segmental recurrent neural network for end-to-end acoustic modelling. This model connects the segmental conditional random field (CRF) with a recurrent neural network (RNN) used for feature extraction. Compared to most previous CRF-based acoustic models, it does not rely on an external system to provide features or segmentation boundaries. Instead, this model marginalises out all the possible segmentations, and features are extracted from the RNN trained together with the segmental CRF. In essence, this model is self-contained and can be trained end-to-end. In this paper, we discuss practical training and decoding issues as well as the method to speed up the training in the context of speech recognition. We performed experiments on the TIMIT dataset. We achieved 17.3 phone error rate (PER) from the first-pass decoding --- the best reported result using CRFs, despite the fact that we only used a zeroth-order CRF and without using any language model.

Connected through hyperedge HE-1 (induced by source paper "improving human text comprehension through semi markov crf based neural section title generation"):
  with members:
    - "on the convergence of adam and beyond"
    - "bottom up abstractive summarization"
    - "deep contextualized word representations"
    - "neural document summarization by jointly learning to score and select sentences"
    - "learning recurrent span representations for extractive question answering"
    - "get to the point summarization with pointer generator networks"
    - "challenges in data to document generation"
    - "multitask learning with ctc and segmental crf for speech recognition"

Connected through hyperedge HE-2 (induced by source paper "the history began from alexnet a comprehensive survey on deep learning approaches"):
  with members:
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "an analysis of deep neural network models for practical applications"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "a deep convolutional neural network for background subtraction"
    - "a survey of neuromorphic computing and neural networks in hardware"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (induced by source paper "quaternion convolutional neural networks for end to end automatic speech recognition"):
  with members:
    - "improving speech recognition by revising gated recurrent units"
    - "deep quaternion networks"
    - "deep complex networks"
    - "dynamic routing between capsules"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "multitask learning with ctc and segmental crf for speech recognition"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-5 (induced by source paper "structured attention networks"):
  with members:
    - "textual entailment with structured attentions and composition"
    - "online segment to segment neural transduction"
    - "enhanced lstm for natural language inference"
    - "neural architectures for named entity recognition"
    - "neural tree indexers for text understanding"
    - "the neural noisy channel"
    - "end to end memory networks"
    - "listen attend and spell"

Connected through hyperedge HE-6 (induced by source paper "sequence prediction with neural segmental models"):
  with members:
    - "optimizing expected word error rate via sampling for speech recognition"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"
    - "wav2letter an end to end convnet based speech recognition system"
    - "an embedded segmental k means model for unsupervised segmentation and clustering of speech"
    - "multitask learning with ctc and segmental crf for speech recognition"
    - "end to end training approaches for discriminative segmental models"
    - "advances in all neural speech recognition"

Connected through hyperedge HE-7 (induced by source paper "exploring neural transducers for end to end speech recognition"):
  with members:
    - "online and linear time attention by enforcing monotonic alignments"
    - "english conversational telephone speech recognition by humans and machines"
    - "an online sequence to sequence model for noisy speech recognition"
    - "wav2letter an end to end convnet based speech recognition system"
    - "reducing bias in production speech models"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "dense prediction on sequences with time dilated convolutions for speech recognition"
    - "advances in all neural speech recognition"

Connected through hyperedge HE-8 (induced by source paper "end to end neural segmental models for speech recognition"):
  with members:
    - "direct acoustics to word models for english conversational speech recognition"
    - "wav2letter an end to end convnet based speech recognition system"
    - "multitask learning with ctc and segmental crf for speech recognition"
    - "end to end training approaches for discriminative segmental models"
    - "efficient segmental cascades for speech recognition"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "end to end attention based large vocabulary speech recognition"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"

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)