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 "exploring end to end techniques for low resource speech recognition"):
  Abstract: In this work we present simple grapheme-based system for low-resource speech recognition using Babel data for Turkish spontaneous speech (80 hours). We have investigated different neural network architectures performance, including fully-convolutional, recurrent and ResNet with GRU. Different features and normalization techniques are compared as well. We also proposed CTC-loss modification using segmentation during training, which leads to improvement while decoding with small beam size. Our best model achieved word error rate of 45.8%, which is the best reported result for end-to-end systems using in-domain data for this task, according to our knowledge.

Includes members:
  Member "sequence based multi lingual low resource speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards zero shot learning for automatic phonemic transcription")
  Member "letter based speech recognition with gated convnets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck")
  Member "improved regularization techniques for end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "incorporating end to end speech recognition models for sentiment analysis")
  Member "wav2letter an end to end convnet based speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning robust and multilingual speech representations")
  Member "deep speech 2 end to end speech recognition in english and mandarin":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "good subnetworks provably exist pruning via greedy forward selection")
  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 "deep speech scaling up end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "flexibo cost aware multi objective optimization of deep neural networks")
  Member "sequence transduction with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gradient based adversarial attacks on categorical sequence models via traversing an embedded world")

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.SD(Sound)