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: "trainable frontend for robust and far field keyword spotting"
  Abstract: Robust and far-field speech recognition is critical to enable true hands-free communication. In far-field conditions, signals are attenuated due to distance. To improve robustness to loudness variation, we introduce a novel frontend called per-channel energy normalization (PCEN). The key ingredient of PCEN is the use of an automatic gain control based dynamic compression to replace the widely used static (such as log or root) compression. We evaluate PCEN on the keyword spotting task. On our large rerecorded noisy and far-field eval sets, we show that PCEN significantly improves recognition performance. Furthermore, we model PCEN as neural network layers and optimize high-dimensional PCEN parameters jointly with the keyword spotting acoustic model. The trained PCEN frontend demonstrates significant further improvements without increasing model complexity or inference-time cost.

Connected through hyperedge HE-1 (induced by source paper "robust sound event detection in bioacoustic sensor networks"):
  with members:
    - "learning to detect dysarthria from raw speech"
    - "attention based end to end models for small footprint keyword spotting"
    - "recognizing birds from sound the 2018 birdclef baseline system"
    - "convolutional recurrent neural networks for bird audio detection"
    - "densely connected cnns for bird audio detection"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "deformable convolutional networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-2 (induced by source paper "orthogonality constrained multi head attention for keyword spotting"):
  with members:
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "self attention aligner a latency control end to end model for asr using self attention network and chunk hopping"
    - "attention based end to end models for small footprint keyword spotting"
    - "multi head attention with disagreement regularization"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"
    - "convolutional recurrent neural networks for small footprint keyword spotting"
    - "an end to end architecture for keyword spotting and voice activity detection"

Connected through hyperedge HE-3 (induced by source paper "query by example on device keyword spotting"):
  with members:
    - "donut ctc based query by example keyword spotting"
    - "attention based end to end models for small footprint keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "meta learning for few shot keyword spotting"
    - "fast asr free and almost zero resource keyword spotting using dtw and cnns for humanitarian monitoring"
    - "streaming small footprint keyword spotting using sequence to sequence models"
    - "end to end asr free keyword search from speech"

Connected through hyperedge HE-4 (induced by source paper "learning the helix topology of musical pitch"):
  with members:
    - "extended playing techniques the next milestone in musical instrument recognition"
    - "wavelet scattering on the pitch spiral"
    - "deep convolutional networks on the pitch spiral for musical instrument recognition"

Connected through hyperedge HE-5 (induced by source paper "long distance detection of bioacoustic events with per channel energy normalization"):
  with members:
    - "robust sound event detection in bioacoustic sensor networks"

Connected through hyperedge HE-6 (induced by source paper "learning to detect dysarthria from raw speech"):
  with members:
    - "end to end speech recognition from the raw waveform"
    - "reducing bias in production speech models"
    - "deep scattering spectrum"
    - "neural machine translation by jointly learning to align and translate"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "supervised speech separation based on deep learning an overview"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "deep attractor network for single microphone speaker separation"
    - "using optimal ratio mask as training target for supervised speech separation"
    - "multi objective learning and mask based post processing for deep neural network based speech enhancement"
    - "segan speech enhancement generative adversarial network"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "a fully convolutional neural network for speech enhancement"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"

Connected through hyperedge HE-8 (induced by source paper "reducing bias in production speech models"):
  with members:
    - "gram ctc automatic unit selection and target decomposition for sequence labelling"
    - "wav2letter an end to end convnet based speech recognition system"
    - "dense prediction on sequences with time dilated convolutions for speech recognition"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "learning multiscale features directly from waveforms"
    - "recurrent batch normalization"
    - "achieving human parity in conversational speech recognition"
    - "deep speech 2 end to end speech recognition in english and mandarin"

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)