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: "recurrent models for auditory attention in multi microphone distance speech recognition"
  Abstract: Integration of multiple microphone data is one of the key ways to achieve robust speech recognition in noisy environments or when the speaker is located at some distance from the input device. Signal processing techniques such as beamforming are widely used to extract a speech signal of interest from background noise. These techniques, however, are highly dependent on prior spatial information about the microphones and the environment in which the system is being used. In this work, we present a neural attention network that directly combines multi-channel audio to generate phonetic states without requiring any prior knowledge of the microphone layout or any explicit signal preprocessing for speech enhancement. We embed an attention mechanism within a Recurrent Neural Network (RNN) based acoustic model to automatically tune its attention to a more reliable input source. Unlike traditional multi-channel preprocessing, our system can be optimized towards the desired output in one step. Although attention-based models have recently achieved impressive results on sequence-to-sequence learning, no attention mechanisms have previously been applied to learn potentially asynchronous and non-stationary multiple inputs. We evaluate our neural attention model on the CHiME-3 challenge task, and show that the model achieves comparable performance to beamforming using a purely data-driven method.

Connected through hyperedge HE-1 (induced by source paper "robust multi channel speech recognition using frequency aligned network"):
  with members:
    - "multichannel end to end speech recognition"

Connected through hyperedge HE-2 (induced by source paper "raim recurrent attentive and intensive model of multimodal patient monitoring data"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"
    - "attention is all you need"
    - "highrisk prediction from electronic medical records via deep attention networks"
    - "deep learning to attend to risk in icu"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "temporal relational reasoning in videos"

Connected through hyperedge HE-3 (induced by source paper "sensor transformation attention networks"):
  with members:
    - "lipnet end to end sentence level lipreading"
    - "attention based multimodal fusion for video description"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "end to end attention based large vocabulary speech recognition"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "distilling knowledge using parallel data for far field speech recognition"):
  with members:
    - "a network of deep neural networks for distant speech recognition"
    - "the ibm 2016 english conversational telephone speech recognition system"
    - "the microsoft 2016 conversational speech recognition system"
    - "transferring knowledge from a rnn to a dnn"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-5 (induced by source paper "end to end models with auditory attention in multi channel keyword spotting"):
  with members:
    - "attention based end to end models for small footprint keyword spotting"
    - "convolutional recurrent neural networks for small footprint keyword spotting"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "on adjusting the learning rate in frequency domain echo cancellation with double talk"

Connected through hyperedge HE-6 (induced by source paper "stream attention for far field multi microphone asr"):
  with members:
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "end to end attention based distant speech recognition with highway lstm"):
  with members:
    - "highway long short term memory rnns for distant speech recognition"
    - "on using monolingual corpora in neural machine translation"
    - "first pass large vocabulary continuous speech recognition using bi directional recurrent dnns"
    - "end to end continuous speech recognition using attention based recurrent nn first results"

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.LG(Machine Learning)