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: "end to end training approaches for discriminative segmental models"
  Abstract: Recent work on discriminative segmental models has shown that they can achieve competitive speech recognition performance, using features based on deep neural frame classifiers. However, segmental models can be more challenging to train than standard frame-based approaches. While some segmental models have been successfully trained end to end, there is a lack of understanding of their training under different settings and with different losses. #R##N#We investigate a model class based on recent successful approaches, consisting of a linear model that combines segmental features based on an LSTM frame classifier. Similarly to hybrid HMM-neural network models, segmental models of this class can be trained in two stages (frame classifier training followed by linear segmental model weight training), end to end (joint training of both frame classifier and linear weights), or with end-to-end fine-tuning after two-stage training. #R##N#We study segmental models trained end to end with hinge loss, log loss, latent hinge loss, and marginal log loss. We consider several losses for the case where training alignments are available as well as where they are not. #R##N#We find that in general, marginal log loss provides the most consistent strong performance without requiring ground-truth alignments. We also find that training with dropout is very important in obtaining good performance with end-to-end training. Finally, the best results are typically obtained by a combination of two-stage training and fine-tuning.

Connected through hyperedge HE-1 (induced by source paper "time contrastive learning based deep bottleneck features for text dependent speaker verification"):
  with members:
    - "end to end neural segmental models for speech recognition"
    - "end to end attention based text dependent speaker verification"
    - "adversarial network bottleneck features for noise robust speaker verification"
    - "sequence prediction with neural segmental models"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "understanding deep learning requires rethinking generalization"
    - "incorporating pass phrase dependent background models for text dependent speaker verification"
    - "unsupervised feature learning from temporal data"

Connected through hyperedge HE-2 (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-3 (induced by source paper "rich character level information for korean morphological analysis and part of speech tagging"):
  with members:
    - "optimizing performance of recurrent neural networks on gpus"
    - "bidirectional lstm crf models for sequence tagging"
    - "adam a method for stochastic optimization"
    - "representing text chunks"

Connected through hyperedge HE-4 (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"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "advances in all neural speech recognition"

Connected through hyperedge HE-5 (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"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "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)