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 "guiding ctc posterior spike timings for improved posterior fusion and knowledge distillation"):
  Abstract: Conventional automatic speech recognition (ASR) systems trained from frame-level alignments can easily leverage posterior fusion to improve ASR accuracy and build a better single model with knowledge distillation. End-to-end ASR systems trained using the Connectionist Temporal Classification (CTC) loss do not require frame-level alignment and hence simplify model training. However, sparse and arbitrary posterior spike timings from CTC models pose a new set of challenges in posterior fusion from multiple models and knowledge distillation between CTC models. We propose a method to train a CTC model so that its spike timings are guided to align with those of a pre-trained guiding CTC model. As a result, all models that share the same guiding model have aligned spike timings. We show the advantage of our method in various scenarios including posterior fusion of CTC models and knowledge distillation between CTC models with different architectures. With the 300-hour Switchboard training data, the single word CTC model distilled from multiple models improved the word error rates to 13.7%/23.1% from 14.9%/24.1% on the Hub5 2000 Switchboard/CallHome test sets without using any data augmentation, language model, or complex decoder.

Includes members:
  Member "improved training for online end to end speech recognition systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "end to end speech recognition using a high rank lstm ctc based model")
  Member "english conversational telephone speech recognition by humans and machines":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed training of deep neural network acoustic models for automatic speech recognition")
  Member "building competitive direct acoustics to word models for english conversational speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search")
  Member "direct acoustics to word models for english conversational speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "acoustic to word models with conversational context information")
  Member "gram ctc automatic unit selection and target decomposition for sequence labelling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards using context dependent symbols in ctc without state tying decision trees")
  Member "residual convolutional ctc networks for automatic speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial camouflage hiding physical world attacks with natural styles")
  Member "simplified end to end mmi training and voting for asr":
    also connected through other hyperedges:
      (none)
  Member "advances in all neural speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning for audio signal processing")

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)