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: "multitask learning with ctc and segmental crf for speech recognition"
  Abstract: Segmental conditional random fields (SCRFs) and connectionist temporal classification (CTC) are two sequence labeling methods used for end-to-end training of speech recognition models. Both models define a transcription probability by marginalizing decisions about latent segmentation alternatives to derive a sequence probability: the former uses a globally normalized joint model of segment labels and durations, and the latter classifies each frame as either an output symbol or a "continuation" of the previous label. In this paper, we train a recognition model by optimizing an interpolation between the SCRF and CTC losses, where the same recurrent neural network (RNN) encoder is used for feature extraction for both outputs. We find that this multitask objective improves recognition accuracy when decoding with either the SCRF or CTC models. Additionally, we show that CTC can also be used to pretrain the RNN encoder, which improves the convergence rate when learning the joint 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"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "neural phrase to phrase machine translation"):
  with members:
    - "phrase based attentions"
    - "modeling localness for self attention networks"
    - "neural machine translation decoding with terminology constraints"
    - "phrase table as recommendation memory for neural machine translation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "translating phrases in neural machine translation"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-3 (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"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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"
    - "end to end training approaches for discriminative segmental models"
    - "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 "improving end to end speech recognition with policy learning"):
  with members:
    - "wav2letter an end to end convnet based speech recognition system"
    - "a deep reinforced model for abstractive summarization"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "learning online alignments with continuous rewards policy gradient"
    - "an actor critic algorithm for sequence prediction"
    - "the microsoft 2016 conversational speech recognition system"
    - "xception deep learning with depthwise separable convolutions"
    - "reward augmented maximum likelihood for neural structured prediction"

Connected through hyperedge HE-6 (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"
    - "end to end training approaches for discriminative segmental models"
    - "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)