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: "forward attention in sequence to sequence acoustic modeling for speech synthesis"
  Abstract: This paper proposes a forward attention method for the sequence-to-sequence acoustic modeling of speech synthesis. This method is motivated by the nature of the monotonic alignment from phone sequences to acoustic sequences. Only the alignment paths that satisfy the monotonic condition are taken into consideration at each decoder timestep. The modified attention probabilities at each timestep are computed recursively using a forward algorithm. A transition agent for forward attention is further proposed, which helps the attention mechanism to make decisions whether to move forward or stay at each decoder timestep. Experimental results show that the proposed forward attention method achieves faster convergence speed and higher stability than the baseline attention method. Besides, the method of forward attention with transition agent can also help improve the naturalness of synthetic speech and control the speed of synthetic speech effectively.

Connected through hyperedge HE-1 (induced by source paper "espnet tts unified reproducible and integratable open source end to end text to speech toolkit"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "jvs corpus free japanese multi speaker voice corpus"
    - "libritts a corpus derived from librispeech for text to speech"
    - "mixed precision training for nlp and speech recognition with openseq2seq"
    - "back translation style data augmentation for end to end asr"
    - "waveglow a flow based generative network for speech synthesis"
    - "espnet end to end speech processing toolkit"
    - "close to human quality tts with transformer"

Connected through hyperedge HE-2 (induced by source paper "sequence to sequence acoustic modeling for voice conversion"):
  with members:
    - "neural voice cloning with a few samples"
    - "fitting new speakers based on a short untranscribed sample"
    - "voice conversion using sequence to sequence learning of context posterior probabilities"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "attention is all you need"
    - "tacotron towards end to end speech synthesis"
    - "deep voice 3 2000 speaker neural text to speech"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-3 (induced by source paper "robust sequence to sequence acoustic modeling with stepwise monotonic attention for neural tts"):
  with members:
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "online and linear time attention by enforcing monotonic alignments"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "tacotron towards end to end speech synthesis"
    - "local monotonic attention mechanism for end to end speech and language processing"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "wavenet a generative model for raw audio"
    - "morphological inflection generation with hard monotonic attention"

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)