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: "character level incremental speech recognition with recurrent neural networks"
  Abstract: In real-time speech recognition applications, the latency is an important issue. We have developed a character-level incremental speech recognition (ISR) system that responds quickly even during the speech, where the hypotheses are gradually improved while the speaking proceeds. The algorithm employs a speech-to-character unidirectional recurrent neural network (RNN), which is end-to-end trained with connectionist temporal classification (CTC), and an RNN-based character-level language model (LM). The output values of the CTC-trained RNN are character-level probabilities, which are processed by beam search decoding. The RNN LM augments the decoding by providing long-term dependency information. We propose tree-based online beam search with additional depth-pruning, which enables the system to process infinitely long input speech with low latency. This system not only responds quickly on speech but also can dictate out-of-vocabulary (OOV) words according to pronunciation. The proposed model achieves the word error rate (WER) of 8.90% on the Wall Street Journal (WSJ) Nov'92 20K evaluation set when trained on the WSJ SI-284 training set.

Connected through hyperedge HE-1 (induced by source paper "who needs words lexicon free speech recognition"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "fully convolutional speech recognition"
    - "wav2letter the fastest open source speech recognition system"
    - "improved training of end to end attention models for speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "comparison of decoding strategies for ctc acoustic models"
    - "wav2letter an end to end convnet based speech recognition system"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "phoneme level language models for sequence based low resource asr"):
  with members:
    - "domain robust feature extraction for rapid low resource asr development"
    - "sequence based multi lingual low resource speech recognition"
    - "comparison of decoding strategies for ctc acoustic models"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"
    - "enriching word vectors with subword information"
    - "visualizing and understanding recurrent networks"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "first pass large vocabulary continuous speech recognition using bi directional recurrent dnns"

Connected through hyperedge HE-3 (induced by source paper "a limited size ensemble of homogeneous cnn lstms for high performance word classification"):
  with members:
    - "a large scale field test on word image classification in large historical document collections using a traditional and two deep learning methods"
    - "handwriting recognition using cohort of lstm and lexicon verification with extremely large lexicon"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "improved deep convolutional neural network for online handwritten chinese character recognition using domain specific knowledge"
    - "constructing long short term memory based deep recurrent neural networks for large vocabulary speech recognition"
    - "adadelta an adaptive learning rate method"
    - "ocr post processing error correction algorithm using google online spelling suggestion"

Connected through hyperedge HE-4 (induced by source paper "hierarchical multi task learning with ctc"):
  with members:
    - "hierarchical multitask learning for ctc based speech recognition"
    - "sequence based multi lingual low resource speech recognition"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "state of the art speech recognition with sequence to sequence models"
    - "subword and crossword units for ctc acoustic models"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"

Connected through hyperedge HE-5 (induced by source paper "comparison of decoding strategies for ctc acoustic models"):
  with members:
    - "multiplicative lstm for sequence modelling"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "advances in all neural speech recognition"
    - "input switched affine networks an rnn architecture designed for interpretability"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "listen attend and spell"
    - "end to end attention based large vocabulary speech recognition"

Connected through hyperedge HE-6 (induced by source paper "generative knowledge transfer for neural language models"):
  with members:
    - "learning privately from multiparty data"
    - "numerically grounded language models for semantic error correction"
    - "wavenet a generative model for raw audio"
    - "dependency recurrent neural language models for sentence completion"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "transferring knowledge from a rnn to a dnn"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-7 (induced by source paper "character level language modeling with hierarchical recurrent neural networks"):
  with members:
    - "gated word character recurrent language model"
    - "lightrnn memory and computation efficient recurrent neural networks"
    - "exploring the limits of language modeling"
    - "single stream parallelization of generalized lstm like rnns on a gpu"
    - "character based neural machine translation"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "character aware neural language models"
    - "blackout speeding up recurrent neural network language models with very large vocabularies"

Connected through hyperedge HE-8 (induced by source paper "fpga based low power speech recognition with recurrent neural networks"):
  with members:
    - "eie efficient inference engine on compressed deep neural network"
    - "fpga based implementation of deep neural networks using on chip memory only"
    - "single stream parallelization of generalized lstm like rnns on a gpu"
    - "fixed point performance analysis of recurrent neural networks"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "adadelta an adaptive learning rate method"

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)