human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "effectiveness of self supervised pre training for speech recognition"
  Abstract: We present pre-training approaches for self-supervised representation learning of speech data. A BERT, masked language model, loss on discrete features is compared with an InfoNCE-based constrastive loss on continuous speech features. The pre-trained models are then fine-tuned with a Connectionist Temporal Classification (CTC) loss to predict target character sequences. To study impact of stacking multiple feature learning modules trained using different self-supervised loss functions, we test the discrete and continuous BERT pre-training approaches on spectral features and on learned acoustic representations, showing synergitic behaviour between acoustically motivated and masked language model loss functions. In low-resource conditions using only 10 hours of labeled data, we achieve Word Error Rates (WER) of 10.2\% and 23.5\% on the standard test "clean" and "other" benchmarks of the Librispeech dataset, which is almost on bar with previously published work that uses 10 times more labeled data. Moreover, compared to previous work that uses two models in tandem, by using one model for both BERT pre-trainining and fine-tuning, our model provides an average relative WER reduction of 9%.

Question: Please tell me which class the center node 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)