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: "honk a pytorch reimplementation of convolutional neural networks for keyword spotting"
  Abstract: We describe Honk, an open-source PyTorch reimplementation of convolutional neural networks for keyword spotting that are included as examples in TensorFlow. These models are useful for recognizing "command triggers" in speech-based interfaces (e.g., "Hey Siri"), which serve as explicit cues for audio recordings of utterances that are sent to the cloud for full speech recognition. Evaluation on Google's recently released Speech Commands Dataset shows that our reimplementation is comparable in accuracy and provides a starting point for future work on the keyword spotting task.

Connected through hyperedge HE-1 (induced by source paper "prototypical metric transfer learning for continuous speech keyword spotting with limited training data"):
  with members:
    - "training neural audio classifiers with few data"
    - "prototypical networks for few shot learning"
    - "transfer learning for speech recognition on a budget"
    - "transfer learning for music classification and regression tasks"
    - "matching networks for one shot learning"
    - "deep residual learning for image recognition"
    - "deep speech scaling up end to end speech recognition"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-2 (induced by source paper "a monaural speech enhancement method for robust small footprint keyword spotting"):
  with members:
    - "attention based end to end models for small footprint keyword spotting"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "supervised speech separation based on deep learning an overview"
    - "deep residual learning for small footprint keyword spotting"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "multimodal uncertainty reduction for intention recognition in human robot interaction"):
  with members:
    - "multimodal signal processing and learning aspects of human robot interaction for an assistive bathing robot"
    - "starting engagement detection towards a companion robot using multimodal features"

Connected through hyperedge HE-4 (induced by source paper "recent advances in deep learning an overview"):
  with members:
    - "deep learning a critical appraisal"
    - "adversarial attacks on neural network policies"
    - "deep learning for environmentally robust speech recognition an overview of recent developments"
    - "lstm a search space odyssey"
    - "a knowledge grounded neural conversation model"
    - "me r cnn multi expert region based cnn for object detection"
    - "on the origin of deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "deep residual learning for small footprint keyword spotting"):
  with members:
    - "the microsoft 2017 conversational speech recognition system"
    - "convolutional recurrent neural networks for small footprint keyword spotting"
    - "max pooling loss training of long short term memory networks for small footprint keyword spotting"
    - "the microsoft 2016 conversational speech recognition system"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "an experimental analysis of the power consumption of convolutional neural networks for keyword spotting"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "convolutional recurrent neural networks for small footprint keyword spotting"
    - "max pooling loss training of long short term memory networks for small footprint keyword spotting"

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)