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: "sequence to sequence neural net models for grapheme to phoneme conversion"
  Abstract: Sequence-to-sequence translation methods based on generation with a side-conditioned language model have recently shown promising results in several tasks. In machine translation, models conditioned on source side words have been used to produce target-language text, and in image captioning, models conditioned images have been used to generate caption text. Past work with this approach has focused on large vocabulary tasks, and measured quality in terms of BLEU. In this paper, we explore the applicability of such models to the qualitatively different grapheme-to-phoneme task. Here, the input and output side vocabularies are small, plain n-gram models do well, and credit is only given when the output is exactly correct. We find that the simple side-conditioned generation approach is able to rival the state-of-the-art, and we are able to significantly advance the stat-of-the-art with bi-directional long short-term memory (LSTM) neural networks that use the same alignment information that is used in conventional approaches.

Connected through hyperedge HE-1 (induced by source paper "token level ensemble distillation for grapheme to phoneme conversion"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "the evolved transformer"
    - "universal transformers"
    - "achieving human parity on automatic chinese to english news translation"
    - "attending to mathematical language with transformers"
    - "sentence wise smooth regularization for sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "natural language generation with neural variational models"):
  with members:
    - "probabilistic natural language generation with wasserstein autoencoders"
    - "learning to ask neural question generation for reading comprehension"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "latent variable dialogue models and their diversity"
    - "adversarial learning for neural dialogue generation"
    - "variational attention for sequence to sequence models"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-3 (induced by source paper "zero shot keyword spotting for visual speech recognition in the wild"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "espnet end to end speech processing toolkit"
    - "end to end audiovisual speech recognition"
    - "lcanet end to end lipreading with cascaded attention ctc"
    - "improving speaker independent lipreading with domain adversarial training"
    - "attention is all you need"
    - "deep word embeddings for visual speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"

Connected through hyperedge HE-4 (induced by source paper "understanding and mitigating the security risks of voice controlled third party skills on amazon alexa and google home"):
  with members:
    - "continuous authentication for voice assistants"
    - "dolphinattack inaudible voice commands"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "6thsense a context aware sensor based attack detector for smart devices"
    - "neural paraphrase generation with stacked residual lstm networks"
    - "a large annotated corpus for learning natural language inference"
    - "your voice assistant is mine how to abuse speakers to steal information and control your phone"

Connected through hyperedge HE-5 (induced by source paper "design challenges in named entity transliteration"):
  with members:
    - "attention is all you need"
    - "sequence to sequence neural network models for transliteration"
    - "character based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "simple models for word formation in english slang"):
  with members:
    - "charmanteau character embedding models for portmanteau creation"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "part of speech tagging with bidirectional long short term memory recurrent neural network"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "tfw damngina juvie and hotsie totsie on the linguistic and social aspects of internet slang"):
  with members:
    - "the extreme value machine"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "equality of opportunity in supervised learning"
    - "semantics derived automatically from language corpora necessarily contain human biases"
    - "quantifying and reducing stereotypes in word embeddings"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "a survey on measuring indirect discrimination in machine learning"
    - "learning to understand phrases by embedding the dictionary"

Connected through hyperedge HE-8 (induced by source paper "deep voice real time neural text to speech"):
  with members:
    - "samplernn an unconditional end to end neural audio generation model"
    - "fast wavenet generation algorithm"
    - "quasi recurrent neural networks"
    - "wavenet a generative model for raw audio"
    - "pixel recurrent neural networks"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)