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: "deep learning with convolutional neural networks for eeg decoding and visualization"
  Abstract: Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping. Hum Brain Mapp 38:5391–5420, 2017. © 2017 Wiley Periodicals, Inc.

Connected through hyperedge HE-1 (induced by source paper "collaborative inference for efficient remote monitoring"):
  with members:
    - "deep learning for time series classification a review"
    - "efficient inference on deep neural networks by dynamic representations and decision gates"
    - "deep feature compression for collaborative object detection"
    - "doubly nested network for resource efficient inference"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "xgboost a scalable tree boosting system"
    - "doubly convolutional neural networks"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "ultra efficient transfer learning with meta update for cross subject eeg classification"):
  with members:
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "deep transfer learning for eeg based brain computer interface"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "prototypical networks for few shot learning"
    - "online and offline domain adaptation for reducing bci calibration effort"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"
    - "matching networks for one shot learning"

Connected through hyperedge HE-3 (induced by source paper "motor imagery classification of single arm tasks using convolutional neural network based on feature refining"):
  with members:
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "deep learning of movement intent and reaction time for eeg informed adaptation of rehabilitation robots"):
  with members:
    - "understanding deep learning requires rethinking generalization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "explainable deep learning for video recognition tasks a framework recommendations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "discriminating spatial and temporal relevance in deep taylor decompositions for explainable activity recognition"
    - "metrics for explainable ai challenges and prospects"
    - "real world anomaly detection in surveillance videos"
    - "interpretable to whom a role based model for analyzing interpretable machine learning systems"
    - "evaluating feature importance estimates"
    - "tip typifying the interpretability of procedures"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-6 (induced by source paper "pruning by explaining a novel criterion for deep neural network pruning"):
  with members:
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "importance estimation for neural network pruning"
    - "compact and computationally efficient representation of deep neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "nisp pruning networks using neuron importance score propagation"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "structured probabilistic pruning for convolutional neural network acceleration"

Connected through hyperedge HE-7 (induced by source paper "on the vulnerability of cnn classifiers in eeg based bcis"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial patch"
    - "adversarial spheres"
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "audio adversarial examples targeted attacks on speech to text"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "eeg based user reaction time estimation using riemannian geometry features"
    - "online and offline domain adaptation for reducing bci calibration effort"

Connected through hyperedge HE-8 (induced by source paper "universal adversarial perturbations for cnn classifiers in eeg based bcis"):
  with members:
    - "on the vulnerability of cnn classifiers in eeg based bcis"
    - "universal adversarial perturbations for speech recognition systems"
    - "active learning for black box adversarial attacks in eeg based brain computer interfaces"
    - "adversarial patch"
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "audio adversarial examples targeted attacks on speech to text"
    - "feature selection a data perspective"

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)