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 neural segmental models for speech recognition"
  Abstract: Segmental models are an alternative to frame-based models for sequence prediction, where hypothesized path weights are based on entire segment scores rather than a single frame at a time. Neural segmental models are segmental models that use neural network-based weight functions. Neural segmental models have achieved competitive results for speech recognition, and their end-to-end training has been explored in several studies. In this work, we review neural segmental models, which can be viewed as consisting of a neural network-based acoustic encoder and a finite-state transducer decoder. We study end-to-end segmental models with different weight functions, including ones based on frame-level neural classifiers and on segmental recurrent neural networks. We study how reducing the search space size impacts performance under different weight functions. We also compare several loss functions for end-to-end training. Finally, we explore training approaches, including multistage versus end-to-end training and multitask training that combines segmental and frame-level losses.

Connected through hyperedge HE-1 (induced by source paper "from semi supervised to almost unsupervised speech recognition with very low resource by jointly learning phonetic structures from audio and text embeddings"):
  with members:
    - "completely unsupervised phoneme recognition by adversarially learning mapping relationships from audio embeddings"
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "almost unsupervised speech recognition with close to zero resource based on phonetic structures learned from very small unpaired speech and text data"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "improved training of wasserstein gans"
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"
    - "an embedded segmental k means model for unsupervised segmentation and clustering of speech"

Connected through hyperedge HE-2 (induced by source paper "time contrastive learning based deep bottleneck features for text dependent speaker verification"):
  with members:
    - "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"
    - "end to end training approaches for discriminative segmental models"
    - "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-3 (induced by source paper "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"):
  with members:
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "an iterative closest point method for unsupervised word translation"
    - "non adversarial unsupervised word translation"
    - "towards unsupervised automatic speech recognition trained by unaligned speech and text only"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "unsupervised learning of semantic audio representations"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"

Connected through hyperedge HE-4 (induced by source paper "letter based speech recognition with gated convnets"):
  with members:
    - "improved training of end to end attention models for speech recognition"
    - "english conversational telephone speech recognition by humans and machines"
    - "improving end to end speech recognition with policy learning"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"
    - "wav2letter an end to end convnet based speech recognition system"
    - "convolutional sequence to sequence learning"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "the microsoft 2016 conversational speech recognition system"

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)