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: "a study of all convolutional encoders for connectionist temporal classification"
  Abstract: Connectionist temporal classification (CTC) is a popular sequence prediction approach for automatic speech recognition that is typically used with models based on recurrent neural networks (RNNs). We explore whether deep convolutional neural networks (CNNs) can be used effectively instead of RNNs as the "encoder" in CTC. CNNs lack an explicit representation of the entire sequence, but have the advantage that they are much faster to train. We present an exploration of CNNs as encoders for CTC models, in the context of character-based (lexicon-free) automatic speech recognition. In particular, we explore a range of one-dimensional convolutional layers, which are particularly efficient. We compare the performance of our CNN-based models against typical RNNbased models in terms of training time, decoding time, model size and word error rate (WER) on the Switchboard Eval2000 corpus. We find that our CNN-based models are close in performance to LSTMs, while not matching them, and are much faster to train and decode.

Connected through hyperedge HE-1 (induced by source paper "controlling for confounders in multimodal emotion classification via adversarial learning"):
  with members:
    - "improving cross corpus speech emotion recognition with adversarial discriminative domain generalization addog"
    - "adversarial training for satire detection controlling for confounding variables"
    - "improving cross corpus speech emotion recognition with adversarial discriminative domain generalization addog"
    - "muse ing on the impact of utterance ordering on crowdsourced emotion annotations"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "mitigating unwanted biases with adversarial learning"
    - "the priori emotion dataset linking mood to emotion detected in the wild"
    - "adversarial removal of demographic attributes from text data"

Connected through hyperedge HE-2 (induced by source paper "end to end text to speech for low resource languages by cross lingual transfer learning"):
  with members:
    - "neural voice cloning with a few samples"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "semi supervised training for improving data efficiency in end to end speech synthesis"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 3 2000 speaker neural text to speech"
    - "natural tts synthesis by conditioning wavenet on mel spectrogram predictions"

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)