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: "highway long short term memory rnns for distant speech recognition"
  Abstract: In this paper, we extend the deep long short-term memory (DLSTM) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers. These direct links, called highway connections, enable unimpeded information flow across different layers and thus alleviate the gradient vanishing problem when building deeper LSTMs. We further introduce the latency-controlled bidirectional LSTMs (BLSTMs) which can exploit the whole history while keeping the latency under control. Efficient algorithms are proposed to train these novel networks using both frame and sequence discriminative criteria. Experiments on the AMI distant speech recognition (DSR) task indicate that we can train deeper LSTMs and achieve better improvement from sequence training with highway LSTMs (HLSTMs). Our novel model obtains $43.9/47.7\%$ WER on AMI (SDM) dev and eval sets, outperforming all previous works. It beats the strong DNN and DLSTM baselines with $15.7\%$ and $5.3\%$ relative improvement respectively.

Connected through hyperedge HE-1 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"
    - "a flexible approach to automated rnn architecture generation"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"

Connected through hyperedge HE-2 (induced by source paper "scaling up online speech recognition using convnets"):
  with members:
    - "reducing transformer depth on demand with structured dropout"
    - "rnn t for latency controlled asr with improved beam search"
    - "a comparative study on transformer vs rnn in speech applications"
    - "end to end asr from supervised to semi supervised learning with modern architectures"
    - "transformer based acoustic modeling for hybrid speech recognition"
    - "streaming end to end speech recognition for mobile devices"
    - "fully convolutional speech recognition"
    - "efficient neural audio synthesis"

Connected through hyperedge HE-3 (induced by source paper "transfer learning from audio visual grounding to speech recognition"):
  with members:
    - "semantic speech retrieval with a visually grounded model of untranscribed speech"
    - "towards visually grounded sub word speech unit discovery"
    - "an unsupervised autoregressive model for speech representation learning"
    - "training augmentation with adversarial examples for robust speech recognition"
    - "extracting domain invariant features by unsupervised learning for robust automatic speech recognition"
    - "deep contextualized word representations"
    - "unsupervised adaptation with interpretable disentangled representations for distant conversational speech recognition"
    - "unsupervised textual grounding linking words to image concepts"

Connected through hyperedge HE-4 (induced by source paper "holodetect few shot learning for error detection"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "a formal framework for probabilistic unclean databases"
    - "on calibration of modern neural networks"
    - "snorkel rapid training data creation with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "wide deep learning for recommender systems"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-5 (induced by source paper "transformer based acoustic modeling for hybrid speech recognition"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "deja vu double feature presentation in deep transformer networks"
    - "a comparative study on transformer vs rnn in speech applications"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "state of the art speech recognition using multi stream self attention with dilated 1d convolutions"
    - "streaming end to end speech recognition for mobile devices"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (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"

Connected through hyperedge HE-7 (induced by source paper "an empirical study of efficient asr rescoring with transformers"):
  with members:
    - "scalable multi corpora neural language models for asr"
    - "spanbert improving pre training by representing and predicting spans"
    - "efficient dynamic wfst decoding for personalized language models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "language modeling with deep transformers"
    - "knowledge distillation for recurrent neural network language modeling with trust regularization"
    - "roberta a robustly optimized bert pretraining approach"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "syntax aware neural semantic role labeling"):
  with members:
    - "syntactic scaffolds for semantic structures"
    - "a full end to end semantic role labeler syntax agnostic over syntax aware"
    - "tag enhanced tree structured neural networks for implicit discourse relation classification"
    - "deep contextualized word representations"
    - "linguistically informed self attention for semantic role labeling"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "improved neural machine translation with a syntax aware encoder and decoder"

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.NE(Neural and Evolutionary Computing)