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: "improving speech recognition by revising gated recurrent units"
  Abstract: Speech recognition is largely taking advantage of deep learning, showing that substantial benefits can be obtained by modern Recurrent Neural Networks (RNNs). The most popular RNNs are Long Short-Term Memory (LSTMs), which typically reach state-of-the-art performance in many tasks thanks to their ability to learn long-term dependencies and robustness to vanishing gradients. Nevertheless, LSTMs have a rather complex design with three multiplicative gates, that might impair their efficient implementation. An attempt to simplify LSTMs has recently led to Gated Recurrent Units (GRUs), which are based on just two multiplicative gates. #R##N#This paper builds on these efforts by further revising GRUs and proposing a simplified architecture potentially more suitable for speech recognition. The contribution of this work is two-fold. First, we suggest to remove the reset gate in the GRU design, resulting in a more efficient single-gate architecture. Second, we propose to replace tanh with ReLU activations in the state update equations. Results show that, in our implementation, the revised architecture reduces the per-epoch training time with more than 30% and consistently improves recognition performance across different tasks, input features, and noisy conditions when compared to a standard GRU.

Connected through hyperedge HE-1 (induced by source paper "cgcnn complex gabor convolutional neural network on raw speech"):
  with members:
    - "phase aware speech enhancement with deep complex u net"
    - "espnet end to end speech processing toolkit"
    - "end to end speech recognition from the raw waveform"
    - "deep complex networks"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "end to end phoneme sequence recognition using convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "a bayesian approach to recurrence in neural networks"):
  with members:
    - "minimal gated unit for recurrent neural networks"
    - "the microsoft 2016 conversational speech recognition system"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "an optimized recurrent unit for ultra low power keyword spotting"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "minimal gated unit for recurrent neural networks"
    - "recurrent neural networks with limited numerical precision"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-4 (induced by source paper "exploring spectro temporal features in end to end convolutional neural networks"):
  with members:
    - "end to end speech recognition from the raw waveform"
    - "learning filterbanks from raw speech for phone recognition"
    - "wav2letter an end to end convnet based speech recognition system"
    - "wavenet a generative model for raw audio"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "adam a method for stochastic optimization"
    - "deep scattering spectrum"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "quaternion convolutional neural networks for end to end automatic speech recognition"):
  with members:
    - "deep quaternion networks"
    - "deep complex networks"
    - "dynamic routing between capsules"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "multitask learning with ctc and segmental crf for speech recognition"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-6 (induced by source paper "improving gated recurrent unit based acoustic modeling with batch normalization and enlarged context"):
  with members:
    - "deep fsmn for large vocabulary continuous speech recognition"
    - "gated recurrent unit based acoustic modeling with future context"
    - "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"
    - "recurrent batch normalization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "feedforward sequential memory networks a new structure to learn long term dependency"
    - "highway long short term memory rnns for distant speech recognition"
    - "thchs 30 a free chinese speech corpus"

Connected through hyperedge HE-7 (induced by source paper "complex gated recurrent neural networks"):
  with members:
    - "gated orthogonal recurrent units on learning to forget"
    - "deep complex networks"
    - "convolutional sequence to sequence learning"
    - "on human motion prediction using recurrent neural networks"
    - "associative long short term memory"
    - "on complex valued convolutional neural networks"
    - "unitary evolution recurrent neural networks"
    - "structural rnn deep learning on spatio temporal graphs"

Connected through hyperedge HE-8 (induced by source paper "gated recurrent unit based acoustic modeling with future context"):
  with members:
    - "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "feedforward sequential memory networks a new structure to learn long term dependency"
    - "highway long short term memory rnns for distant speech recognition"
    - "thchs 30 a free chinese speech corpus"
    - "long short term memory based recurrent neural network architectures for large vocabulary 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)