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 "multitask learning with low level auxiliary tasks for encoder decoder based speech recognition"):
  Abstract: End-to-end training of deep learning-based models allows for implicit learning of intermediate representations based on the final task loss. However, the end-to-end approach ignores the useful domain knowledge encoded in explicit intermediate-level supervision. We hypothesize that using intermediate representations as auxiliary supervision at lower levels of deep networks may be a good way of combining the advantages of end-to-end training and more traditional pipeline approaches. We present experiments on conversational speech recognition where we use lower-level tasks, such as phoneme recognition, in a multitask training approach with an encoder-decoder model for direct character transcription. We compare multiple types of lower-level tasks and analyze the effects of the auxiliary tasks. Our results on the Switchboard corpus show that this approach improves recognition accuracy over a standard encoder-decoder model on the Eval2000 test set.

Includes members:
  Member "learning to parse and translate improves neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "deep networks with stochastic depth":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on feature normalization and data augmentation")
  Member "joint ctc attention based end to end speech recognition using multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "google s multilingual neural machine translation system enabling zero shot translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention over parameters for dialogue systems")
  Member "end to end attention based large vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "eesen end to end speech recognition using deep rnn models and wfst based decoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards zero shot learning for automatic phonemic transcription")
  Member "scheduled sampling for sequence prediction with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "videoflow a conditional flow based model for stochastic video generation")
  Member "attention based models for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient content based sparse attention with routing transformers")

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)