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 "linguistic search optimization for deep learning based lvcsr"):
  Abstract: Recent advances in deep learning based large vocabulary con- tinuous speech recognition (LVCSR) invoke growing demands in large scale speech transcription. The inference process of a speech recognizer is to find a sequence of labels whose corresponding acoustic and language models best match the input feature [1]. The main computation includes two stages: acoustic model (AM) inference and linguistic search (weighted finite-state transducer, WFST). Large computational overheads of both stages hamper the wide application of LVCSR. Benefit from stronger classifiers, deep learning, and more powerful computing devices, we propose general ideas and some initial trials to solve these fundamental problems.

Includes members:
  Member "a gpu based wfst decoder with exact lattice generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural zero inflated quality estimation model for automatic speech recognition system")
  Member "progressive joint modeling in unsupervised single channel overlapped speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "auxiliary interference speaker loss for target speaker speech recognition")
  Member "on modular training of neural acoustics to word model for lvcsr":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learned in speech recognition contextual acoustic word embeddings")
  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 "personalized speech recognition on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks")
  Member "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning for audio signal processing")
  Member "fast and accurate recurrent neural network acoustic models for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks")

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)