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 "gpu accelerated viterbi exact lattice decoder for batched online and offline speech recognition"):
  Abstract: We present an optimized weighted finite-state transducer (WFST) decoder capable of online streaming and offline batch processing of audio using Graphics Processing Units (GPUs). The decoder is efficient in memory utilization, input/output bandwidth, and uses a novel Viterbi implementation designed to maximize parallelism. Memory savings enable the decoder to process larger graphs than previously possible while simultaneously supporting larger numbers of consecutive streams. GPU preprocessing of lattice segments enable intermediate lattice results to be returned to the requestor during streaming inference. Collectively, the proposed improvements achieve up to a 240x speedup over single core CPU decoding, and up to 40x faster decoding than the current state-of-the-art GPU decoder, while returning equivalent results. This architecture also makes deployment of production-grade models on hardware ranging from large data center servers to low-power edge devices practical.

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 "streaming end to end speech recognition for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition")
  Member "decoding with finite state transducers on gpus":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a gpu based wfst decoder with exact lattice generation")
  Member "personalized speech recognition on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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)