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: "neural networks based eeg speech models"
  Abstract: In this paper, we describe three neural network (NN) based EEG-Speech (NES) models that map the unspoken EEG signals to the corresponding phonemes. Instead of using conventional feature extraction techniques, the proposed NES models rely on graphic learning to project both EEG and speech signals into deep representation feature spaces. This NN based linear projection helps to realize multimodal data fusion (i.e., EEG and acoustic signals). It is convenient to construct the mapping between unspoken EEG signals and phonemes. Specifically, among three NES models, two augmented models (i.e., IANES-B and IANES-G) include spoken EEG signals as either bias or gate information to strengthen the feature learning and translation of unspoken EEG signals. A combined unsupervised and supervised training is implemented stepwise to learn the mapping for all three NES models. To enhance the computational performance, three way factored NN training technique is applied to IANES-G model. Unlike many existing methods, our augmented NES models incorporate spoken-EEG signals that can efficiently suppress the artifacts in unspoken-EEG signals. Experimental results reveal that all three proposed NES models outperform the baseline SVM method, whereas IANES-G demonstrates the best performance on speech recovery and classification task comparatively.

Connected through hyperedge HE-1 (induced by source paper "speak your mind towards imagined speech recognition with hierarchical deep learning"):
  with members:
    - "hierarchical deep feature learning for decoding imagined speech from eeg"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals"
    - "xgboost a scalable tree boosting system"
    - "wavenet a generative model for raw audio"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "deep learning the eeg manifold for phonological categorization from active thoughts"):
  with members:
    - "hierarchical deep feature learning for decoding imagined speech from eeg"
    - "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-3 (induced by source paper "speech recognition with no speech or with noisy speech"):
  with members:
    - "robust speech recognition using generative adversarial networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "deepfeat a bottom up and top down saliency model based on deep features of convolutional neural nets"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "end to end saliency mapping via probability distribution prediction"
    - "visualizing residual networks"
    - "a deep multi level network for saliency prediction"
    - "deepgaze ii reading fixations from deep features trained on object recognition"
    - "a deep spatial contextual long term recurrent convolutional network for saliency detection"
    - "what do different evaluation metrics tell us about saliency models"
    - "enhanced factored three way restricted boltzmann machines for speech detection"

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.SD(Sound)