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: "english conversational telephone speech recognition by humans and machines"
  Abstract: One of the most difficult speech recognition tasks is accurate recognition of human to human communication. Advances in deep learning over the last few years have produced major speech recognition improvements on the representative Switchboard conversational corpus. Word error rates that just a few years ago were 14% have dropped to 8.0%, then 6.6% and most recently 5.8%, and are now believed to be within striking range of human performance. This then raises two issues - what IS human performance, and how far down can we still drive speech recognition error rates? A recent paper by Microsoft suggests that we have already achieved human performance. In trying to verify this statement, we performed an independent set of human performance measurements on two conversational tasks and found that human performance may be considerably better than what was earlier reported, giving the community a significantly harder goal to achieve. We also report on our own efforts in this area, presenting a set of acoustic and language modeling techniques that lowered the word error rate of our own English conversational telephone LVCSR system to the level of 5.5%/10.3% on the Switchboard/CallHome subsets of the Hub5 2000 evaluation, which - at least at the writing of this paper - is a new performance milestone (albeit not at what we measure to be human performance!). On the acoustic side, we use a score fusion of three models: one LSTM with multiple feature inputs, a second LSTM trained with speaker-adversarial multi-task learning and a third residual net (ResNet) with 25 convolutional layers and time-dilated convolutions. On the language modeling side, we use word and character LSTMs and convolutional WaveNet-style language models.

Connected through hyperedge HE-1 (induced by source paper "distributed training of deep neural network acoustic models for automatic speech recognition"):
  with members:
    - "distributed deep learning strategies for automatic speech recognition"
    - "realizing petabyte scale acoustic modeling"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "sparse communication for distributed gradient descent"
    - "adam a method for stochastic optimization"
    - "deep speech scaling up end to end speech recognition"

Connected through hyperedge HE-2 (induced by source paper "acoustic to word models with conversational context information"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "advancing acoustic to word ctc model"
    - "hierarchical multi task learning with ctc"
    - "dialog context aware end to end speech recognition"
    - "multi modal data augmentation for end to end asr"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "improved training of end to end attention models for speech recognition"
    - "building competitive direct acoustics to word models for english conversational speech recognition"

Connected through hyperedge HE-3 (induced by source paper "improving oov detection and resolution with external language models in acoustic to word asr"):
  with members:
    - "advancing acoustic to word ctc model"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "acoustic to word model without oov"
    - "comparing human and machine errors in conversational speech transcription"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "tying word vectors and word classifiers a loss framework for language modeling"

Connected through hyperedge HE-4 (induced by source paper "training asr models by generation of contextual information"):
  with members:
    - "transformers with convolutional context for asr"
    - "a comparative study on transformer vs rnn in speech applications"
    - "lessons from building acoustic models with a million hours of speech"
    - "wav2vec unsupervised pre training for speech recognition"
    - "roberta a robustly optimized bert pretraining approach"
    - "hierarchical neural story generation"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "from speech chain to multimodal chain leveraging cross modal data augmentation for semi supervised learning"):
  with members:
    - "deep audio visual speech recognition"
    - "towards robust neural machine translation"
    - "multi scale alignment and contextual history for attention mechanism in sequence to sequence model"
    - "machine speech chain with one shot speaker adaptation"
    - "turbo learning for captionbot and drawingbot"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "the microsoft 2017 conversational speech recognition system"
    - "end to end audiovisual fusion with lstms"

Connected through hyperedge HE-6 (induced by source paper "end to end asr from supervised to semi supervised learning with modern architectures"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "reducing transformer depth on demand with structured dropout"
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "word level speech recognition with a dynamic lexicon"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self training for end to end speech recognition"
    - "state of the art speech recognition using multi stream self attention with dilated 1d convolutions"

Connected through hyperedge HE-7 (induced by source paper "guiding ctc posterior spike timings for improved posterior fusion and knowledge distillation"):
  with members:
    - "improved training for online end to end speech recognition systems"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"
    - "residual convolutional ctc networks for automatic speech recognition"
    - "simplified end to end mmi training and voting for asr"
    - "advances in all neural speech recognition"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"

Connected through hyperedge HE-8 (induced by source paper "attentive adversarial learning for domain invariant training"):
  with members:
    - "conditional teacher student learning"
    - "adversarial speaker adaptation"
    - "adversarial speaker verification"
    - "domain adversarial training for accented speech recognition"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "attention is all you need"
    - "segan speech enhancement generative adversarial network"

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)