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: "improving end to end speech recognition with policy learning"
  Abstract: Connectionist temporal classification (CTC) is widely used for maximum likelihood learning in end-to-end speech recognition models. However, there is usually a disparity between the negative maximum likelihood and the performance metric used in speech recognition, e.g., word error rate (WER). This results in a mismatch between the objective function and metric during training. We show that the above problem can be mitigated by jointly training with maximum likelihood and policy gradient. In particular, with policy learning we are able to directly optimize on the (otherwise non-differentiable) performance metric. We show that joint training improves relative performance by 4% to 13% for our end-to-end model as compared to the same model learned through maximum likelihood. The model achieves 5.53% WER on Wall Street Journal dataset, and 5.42% and 14.70% on Librispeech test-clean and test-other set, respectively.

Connected through hyperedge HE-1 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "fitting new speakers based on a short untranscribed sample"
    - "recurrent fusion network for image captioning"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "letter based speech recognition with gated convnets"
    - "attention is all you need"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "cif continuous integrate and fire for end to end speech recognition"):
  with members:
    - "transformers with convolutional context for asr"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self attention aligner a latency control end to end model for asr using self attention network and chunk hopping"
    - "espnet end to end speech processing toolkit"
    - "wav2letter the fastest open source speech recognition system"
    - "an online attention based model for speech recognition"
    - "aishell 2 transforming mandarin asr research into industrial scale"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-3 (induced by source paper "universal adversarial examples in speech command classification"):
  with members:
    - "universal adversarial audio perturbations"
    - "universal adversarial perturbations for speech recognition systems"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "audio adversarial examples targeted attacks on speech to text"
    - "an overview of vulnerabilities of voice controlled systems"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-4 (induced by source paper "a fully differentiable beam search decoder"):
  with members:
    - "fully convolutional speech recognition"
    - "minimum word error rate training for attention based sequence to sequence models"
    - "wav2letter an end to end convnet based speech recognition system"
    - "convolutional sequence to sequence learning"
    - "six challenges for neural machine translation"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"
    - "cold fusion training seq2seq models together with language models"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-5 (induced by source paper "end to end speech recognition using a high rank lstm ctc based model"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "advancing connectionist temporal classification with attention modeling"
    - "improved training of end to end attention models for speech recognition"
    - "improved training for online end to end speech recognition systems"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "state of the art speech recognition with sequence to sequence models"
    - "exploring neural transducers for end to end speech recognition"
    - "improving the performance of online neural transducer models"

Connected through hyperedge HE-6 (induced by source paper "improved training of end to end attention models for speech recognition"):
  with members:
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"
    - "online and linear time attention by enforcing monotonic alignments"
    - "monotonic chunkwise attention"
    - "letter based speech recognition with gated convnets"
    - "state of the art speech recognition with sequence to sequence models"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"

Connected through hyperedge HE-7 (induced by source paper "on the inductive bias of word character level multi task learning for speech recognition"):
  with members:
    - "advancing acoustic to word ctc model"
    - "hierarchical multi task learning with ctc"
    - "hierarchical multitask learning for ctc based speech recognition"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "optimizing expected word error rate via sampling for speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"
    - "wav2letter an end to end convnet based speech recognition system"

Connected through hyperedge HE-8 (induced by source paper "augmented cyclic adversarial learning for low resource domain adaptation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "few shot adversarial domain adaptation"
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "adversarial discriminative domain adaptation"
    - "self ensembling for visual domain adaptation"

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)