human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sequence prediction with neural segmental models"):
  Abstract: Segments that span contiguous parts of inputs, such as phonemes in speech, named entities in sentences, actions in videos, occur frequently in sequence prediction problems. Segmental models, a class of models that explicitly hypothesizes segments, have allowed the exploration of rich segment features for sequence prediction. However, segmental models suffer from slow decoding, hampering the use of computationally expensive features. #R##N#In this thesis, we introduce discriminative segmental cascades, a multi-pass inference framework that allows us to improve accuracy by adding higher-order features and neural segmental features while maintaining efficiency. We also show that instead of including more features to obtain better accuracy, segmental cascades can be used to speed up training and decoding. #R##N#Segmental models, similarly to conventional speech recognizers, are typically trained in multiple stages. In the first stage, a frame classifier is trained with manual alignments, and then in the second stage, segmental models are trained with manual alignments and the out- puts of the frame classifier. However, obtaining manual alignments are time-consuming and expensive. We explore end-to-end training for segmental models with various loss functions, and show how end-to-end training with marginal log loss can eliminate the need for detailed manual alignments. #R##N#We draw the connections between the marginal log loss and a popular end-to-end training approach called connectionist temporal classification. We present a unifying framework for various end-to-end graph search-based models, such as hidden Markov models, connectionist temporal classification, and segmental models. Finally, we discuss possible extensions of segmental models to large-vocabulary sequence prediction tasks.

Includes members:
  Member "optimizing expected word error rate via sampling for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "minimum bayes risk training of rnn transducer for end to end speech recognition")
  Member "a segmental framework for fully unsupervised large vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning hierarchical discrete linguistic units from visually grounded speech")
  Member "variants of rmsprop and adagrad with logarithmic regret bounds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compositional adam an adaptive compositional solver")
  Member "wav2letter an end to end convnet based speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning robust and multilingual speech representations")
  Member "an embedded segmental k means model for unsupervised segmentation and clustering of speech":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "multitask learning with ctc and segmental crf for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving human text comprehension through semi markov crf based neural section title generation")
  Member "end to end training approaches for discriminative segmental models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "time contrastive learning based deep bottleneck features for text dependent speaker verification")
  Member "segmental recurrent neural networks for end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving human text comprehension through semi markov crf based neural section title generation")

Question: Please tell me which class the source paper 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)