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: "eesen end to end speech recognition using deep rnn models and wfst based decoding"
  Abstract: The performance of automatic speech recognition (ASR) has improved tremendously due to the application of deep neural networks (DNNs). Despite this progress, building a new ASR system remains a challenging task, requiring various resources, multiple training stages and significant expertise. This paper presents our Eesen framework which drastically simplifies the existing pipeline to build state-of-the-art ASR systems. Acoustic modeling in Eesen involves learning a single recurrent neural network (RNN) predicting context-independent targets (phonemes or characters). To remove the need for pre-generated frame labels, we adopt the connectionist temporal classification (CTC) objective function to infer the alignments between speech and label sequences. A distinctive feature of Eesen is a generalized decoding approach based on weighted finite-state transducers (WFSTs), which enables the efficient incorporation of lexicons and language models into CTC decoding. Experiments show that compared with the standard hybrid DNN systems, Eesen achieves comparable word error rates (WERs), while at the same time speeding up decoding significantly.

Connected through hyperedge HE-1 (induced by source paper "towards zero shot learning for automatic phonemic transcription"):
  with members:
    - "multilingual speech recognition with corpus relatedness sampling"
    - "sequence based multi lingual low resource speech recognition"
    - "multilingual bottleneck features for subword modeling in zero resource languages"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "zero shot learning through cross modal transfer"

Connected through hyperedge HE-2 (induced by source paper "espresso a fast end to end neural speech recognition toolkit"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "parallelizable stack long short term memory"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "sequence to sequence speech recognition with time depth separable convolutions"
    - "when does label smoothing help"
    - "who needs words lexicon free speech recognition"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning and adaptive sampling for optimized dnn compilation"):
  with members:
    - "active learning for regression using greedy sampling"
    - "meta gradient reinforcement learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "learning to optimize tensor programs"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "efficient neural architecture search via parameter sharing"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-4 (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"
    - "english conversational telephone speech recognition by humans and machines"

Connected through hyperedge HE-5 (induced by source paper "gated embeddings in end to end speech recognition for conversational context fusion"):
  with members:
    - "advancing acoustic to word ctc model"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improved training of end to end attention models for speech recognition"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "towards language universal end to end speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"

Connected through hyperedge HE-6 (induced by source paper "multimodal abstractive summarization for how2 videos"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "how2 a large scale dataset for multimodal language understanding"
    - "product title refinement via multi modal generative adversarial learning"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "get to the point summarization with pointer generator networks"
    - "attention strategies for multi source sequence to sequence learning"

Connected through hyperedge HE-7 (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"
    - "english conversational telephone speech recognition by humans and machines"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "improving rnn transducer modeling for end to end speech recognition"):
  with members:
    - "advancing acoustic to word ctc model with attention and mixed units"
    - "advancing acoustic to word ctc model"
    - "layer trajectory lstm"
    - "advancing connectionist temporal classification with attention modeling"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "monotonic chunkwise attention"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "exploring neural transducers for end to end speech 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)