human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "improved training of end to end attention models for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scaling up online speech recognition using convnets")
  Member "analyzing hidden representations in end to end automatic speech recognition systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "untangling in invariant speech recognition")
  Member "building competitive direct acoustics to word models for english conversational speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search")
  Member "state of the art speech recognition with sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "subword and crossword units for ctc acoustic models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "acoustic to word models with conversational context information")
  Member "multitask learning with low level auxiliary tasks for encoder decoder based speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mix review alleviate forgetting in the pretrain finetune framework for neural language generation models")
  Member "joint ctc attention based end to end speech recognition using multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")

Question: Please tell me which class the source paper 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)