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: "sequence to sequence asr optimization via reinforcement learning"
  Abstract: Despite the success of sequence-to-sequence approaches in automatic speech recognition (ASR) systems, the models still suffer from several problems, mainly due to the mismatch between the training and inference conditions. In the sequence-to-sequence architecture, the model is trained to predict the grapheme of the current time-step given the input of speech signal and the ground-truth grapheme history of the previous time-steps. However, it remains unclear how well the model approximates real-world speech during inference. Thus, generating the whole transcription from scratch based on previous predictions is complicated and errors can propagate over time. Furthermore, the model is optimized to maximize the likelihood of training data instead of error rate evaluation metrics that actually quantify recognition quality. This paper presents an alternative strategy for training sequence-to-sequence ASR models by adopting the idea of reinforcement learning (RL). Unlike the standard training scheme with maximum likelihood estimation, our proposed approach utilizes the policy gradient algorithm. We can (1) sample the whole transcription based on the model's prediction in the training process and (2) directly optimize the model with negative Levensthein distance as the reward. Experimental results demonstrate that we significantly improved the performance compared to a model trained only with maximum likelihood estimation.

Connected through hyperedge HE-1 (induced by source paper "optimal completion distillation for sequence learning"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "token level and sequence level loss smoothing for rnn language models"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "convergence of value aggregation for imitation learning"
    - "softmax q distribution estimation for structured prediction a theoretical interpretation for raml"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-2 (induced by source paper "end to end speech recognition from the raw waveform"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "the microsoft 2017 conversational speech recognition system"
    - "improving end to end speech recognition with policy learning"
    - "learning filterbanks from raw speech for phone recognition"
    - "wav2letter an end to end convnet based speech recognition system"
    - "attention based wav2text with feature transfer learning"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "instance normalization the missing ingredient for fast stylization"

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)