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: "mixed precision training for nlp and speech recognition with openseq2seq"
  Abstract: We present OpenSeq2Seq - a TensorFlow-based toolkit for training sequence-to-sequence models that features distributed and mixed-precision training. Benchmarks on machine translation and speech recognition tasks show that models built using OpenSeq2Seq give state-of-the-art performance at 1.5-3x less training time. OpenSeq2Seq currently provides building blocks for models that solve a wide range of tasks including neural machine translation, automatic speech recognition, and speech synthesis.

Connected through hyperedge HE-1 (induced by source paper "pytorchpipe a framework for rapid prototyping of pipelines combining language and vision"):
  with members:
    - "ludwig a type based declarative deep learning toolbox"
    - "leveraging medical visual question answering with supporting facts"
    - "trends in integration of vision and language research a survey of tasks datasets and methods"
    - "the visual qa devil in the details the impact of early fusion and batch norm on clevr"
    - "object based reasoning in vqa"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "compositional attention networks for machine reasoning"
    - "show ask attend and answer a strong baseline for visual question answering"

Connected through hyperedge HE-2 (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"
    - "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"
    - "forward attention in sequence to sequence acoustic modeling for speech synthesis"

Connected through hyperedge HE-3 (induced by source paper "mixed precision training with 8 bit floating point"):
  with members:
    - "mixed precision training of convolutional neural networks using integer operations"
    - "nvidia tensor core programmability performance precision"
    - "training and inference with integers in deep neural networks"
    - "high accuracy low precision training"
    - "training deep neural networks with 8 bit floating point numbers"
    - "a call for clarity in reporting bleu scores"
    - "attention is all you need"
    - "deep learning with low precision by half wave gaussian quantization"

Connected through hyperedge HE-4 (induced by source paper "densifying assumed sparse tensors improving memory efficiency and mpi collective performance during tensor accumulation for parallelized training of neural machine translation models"):
  with members:
    - "scaling neural machine translation"
    - "training tips for the transformer model"
    - "attention is all you need"
    - "squeeze and excitation networks"
    - "wav2letter an end to end convnet based speech recognition system"
    - "convolutional sequence to sequence learning"
    - "aggregated residual transformations for deep neural networks"
    - "google s multilingual neural machine translation system enabling zero shot translation"

Connected through hyperedge HE-5 (induced by source paper "tts skins speaker conversion via asr"):
  with members:
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "sample efficient adaptive text to speech"
    - "fitting new speakers based on a short untranscribed sample"
    - "a universal music translation network"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "wav2letter an end to end convnet based speech recognition system"
    - "deep voice 2 multi speaker neural text to speech"

Connected through hyperedge HE-6 (induced by source paper "end to end anchored speech recognition"):
  with members:
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "end to end contextual speech recognition using class language models and a token passing decoder"
    - "on modular training of neural acoustics to word model for lvcsr"
    - "deep extractor network for target speaker recovery from single channel speech mixtures"
    - "state of the art speech recognition with sequence to sequence models"
    - "end to end text dependent speaker verification"
    - "attention based models for speech recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "adaptive loss scaling for mixed precision training"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "mixed precision training with 8 bit floating point"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "accumulation bit width scaling for ultra low precision training of deep networks"
    - "training deep neural networks with 8 bit floating point numbers"
    - "mixed precision training"
    - "ssd single shot multibox detector"
    - "deep residual learning for image recognition"

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)