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 "open source automatic speech recognition for german"):
  Abstract: High quality Automatic Speech Recognition (ASR) is a prerequisite for speech-based applications and research. While state-of-the-art ASR software is freely available, the language dependent acoustic models are lacking for languages other than English, due to the limited amount of freely available training data.  We train acoustic models for German with Kaldi on two datasets, which are both distributed under a Creative Commons license. The resulting model is freely redistributable, lowering the cost of entry for German ASR. The models are trained on a total of 412 hours of German read speech data and we achieve a relative word error reduction of 26% by adding data from the Spoken Wikipedia Corpus to the previously best freely available German acoustic model recipe and dataset. Our best model achieves a word error rate of 14.38 on the Tuda-De test set.  Due to the large amount of speakers and the diversity of topics included in the training data, our model is robust against speaker variation and topic shift.

Includes members:
  Member "the capio 2017 conversational speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sequence to sequence speech recognition with time depth separable convolutions")
  Member "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning robust and multilingual speech representations")
  Member "english conversational telephone speech recognition by humans and machines":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed training of deep neural network acoustic models for automatic speech recognition")

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)