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: "hierarchical multitask learning for ctc based speech recognition"
  Abstract: Previous work has shown that neural encoder-decoder speech recognition can be improved with hierarchical multitask learning, where auxiliary tasks are added at intermediate layers of a deep encoder. We explore the effect of hierarchical multitask learning in the context of connectionist temporal classification (CTC)-based speech recognition, and investigate several aspects of this approach. Consistent with previous work, we observe performance improvements on telephone conversational speech recognition (specifically the Eval2000 test sets) when training a subword-level CTC model with an auxiliary phone loss at an intermediate layer. We analyze the effects of a number of experimental variables (like interpolation constant and position of the auxiliary loss function), performance in lower-resource settings, and the relationship between pretraining and multitask learning. We observe that the hierarchical multitask approach improves over standard multitask training in our higher-data experiments, while in the low-resource settings standard multitask training works well. The best results are obtained by combining hierarchical multitask learning and pretraining, which improves word error rates by 3.4% absolute on the Eval2000 test sets.

Connected through hyperedge HE-1 (induced by source paper "omninet a unified architecture for multi modal multi task learning"):
  with members:
    - "bilinear attention networks"
    - "tvqa localized compositional video question answering"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "attention is all you need"
    - "exploring neural transducers for end to end speech recognition"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "multimodal residual learning for visual qa"

Connected through hyperedge HE-2 (induced by source paper "acoustically grounded word embeddings for improved acoustics to word speech recognition"):
  with members:
    - "advancing acoustic to word ctc model"
    - "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "end to end asr free keyword search from speech"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"

Connected through hyperedge HE-3 (induced by source paper "massively multilingual adversarial speech recognition"):
  with members:
    - "transfer learning of language independent end to end asr with language model fusion"
    - "domain adversarial training for accented speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "listen attend and spell"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "attention based models for speech recognition"

Connected through hyperedge HE-4 (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"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "optimizing expected word error rate via sampling for speech recognition"
    - "improving end to end speech recognition with policy learning"
    - "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-5 (induced by source paper "hierarchical multi task learning with ctc"):
  with members:
    - "sequence based multi lingual low resource speech recognition"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "subword and crossword units for ctc acoustic models"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "character level incremental speech recognition with recurrent neural networks"
    - "neural speech recognizer acoustic to word lstm model for 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)