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: "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
  Abstract: One big challenge that hinders the transition of brain–computer interfaces (BCIs) from laboratory settings to real-life applications is the availability of high-performance and robust learning algorithms that can effectively handle individual differences, i.e., algorithms that can be applied to a new subject with zero or very little subject-specific calibration data. Transfer learning and domain adaptation have been extensively used for this purpose. However, most previous works focused on classification problems. This paper considers an important regression problem in BCI, namely, online driver drowsiness estimation from EEG signals. By integrating fuzzy sets with domain adaptation, we propose a novel online weighted adaptation regularization for regression (OwARR) algorithm to reduce the amount of subject-specific calibration data, and also a source domain selection (SDS) approach to save about half of the computational cost of OwARR. Using a simulated driving dataset with 15 subjects, we show that OwARR and OwARR-SDS can achieve significantly smaller estimation errors than several other approaches. We also provide comprehensive analyses on the robustness of OwARR and OwARR-SDS.

Connected through hyperedge HE-1 (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"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "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-2 (induced by source paper "eeg based drowsiness estimation for driving safety using deep q learning"):
  with members:
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "network decoupling from regular to depthwise separable convolutions"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "learning representations from eeg with deep recurrent convolutional neural networks"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (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"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "eeg based user reaction time estimation using riemannian geometry features"
    - "online and offline domain adaptation for reducing bci calibration effort"

Connected through hyperedge HE-4 (induced by source paper "white box target attack for eeg based bci regression problems"):
  with members:
    - "on the vulnerability of cnn classifiers in eeg based bcis"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "eeg based user reaction time estimation using riemannian geometry features"
    - "spatial filtering for eeg based regression problems in brain computer interface bci"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-5 (induced by source paper "different set domain adaptation for brain computer interfaces a label alignment approach"):
  with members:
    - "open set domain adaptation theoretical bound and algorithm"
    - "open set domain adaptation by backpropagation"
    - "active semi supervised transfer learning astl for offline bci calibration"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "transfer learning enhanced common spatial pattern filtering for brain computer interfaces bcis overview and a new approach"
    - "online and offline domain adaptation for reducing bci calibration effort"
    - "joint geometrical and statistical alignment for visual domain adaptation"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"

Connected through hyperedge HE-6 (induced by source paper "eeg based driver drowsiness estimation using feature weighted episodic training"):
  with members:
    - "episodic training for domain generalization"
    - "dynamically weighted ensemble based prediction system for adaptively modeling driver reaction time"
    - "eeg based driver drowsiness estimation using convolutional neural networks"
    - "attention is all you need"
    - "learning to generalize meta learning for domain generalization"
    - "deeper broader and artier domain generalization"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "on large batch training for deep learning generalization gap and sharp minima"

Connected through hyperedge HE-7 (induced by source paper "manifold embedded knowledge transfer for brain computer interfaces"):
  with members:
    - "on the vulnerability of cnn classifiers in eeg based bcis"
    - "online and offline domain adaptation for reducing bci calibration effort"
    - "joint geometrical and statistical alignment for visual domain adaptation"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"
    - "return of frustratingly easy domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "protecting privacy of users in brain computer interface applications"):
  with members:
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "sharing deep generative representation for perceived image reconstruction from human brain activity"

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)