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: "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression"
  Abstract: There are many important regression problems in real-world brain-computer interface (BCI) applications, e.g., driver drowsiness estimation from EEG signals. This paper considers offline analysis: given a pool of unlabeled EEG epochs recorded during driving, how do we optimally select a small number of them to label so that an accurate regression model can be built from them to label the rest? Active learning is a promising solution to this problem, but interestingly, to our best knowledge, it has not been used for regression problems in BCI so far. This paper proposes a novel enhanced batch-mode active learning (EBMAL) approach for regression, which improves upon a baseline active learning algorithm by increasing the reliability, representativeness and diversity of the selected samples to achieve better regression performance. We validate its effectiveness using driver drowsiness estimation from EEG signals. However, EBMAL is a general approach that can also be applied to many other offline regression problems beyond BCI.

Connected through hyperedge HE-1 (induced by source paper "unsupervised pool based active learning for linear regression"):
  with members:
    - "active learning for regression using greedy sampling"
    - "affect estimation in 3d space using multi task active learning for regression"
    - "pool based sequential active learning for regression"
    - "active learning with statistical models"

Connected through hyperedge HE-2 (induced by source paper "active learning for black box adversarial attacks in eeg based brain computer interfaces"):
  with members:
    - "on the vulnerability of cnn classifiers in eeg based bcis"
    - "active learning for regression using greedy sampling"
    - "affect estimation in 3d space using multi task active learning for regression"
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "black box adversarial attacks with limited queries and information"
    - "audio adversarial examples targeted attacks on speech to text"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "online and offline domain adaptation for reducing bci calibration effort"

Connected through hyperedge HE-3 (induced by source paper "active learning for regression using greedy sampling"):
  with members:
    - "active semi supervised transfer learning astl for offline bci calibration"
    - "pool based sequential active learning for regression"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"

Connected through hyperedge HE-4 (induced by source paper "eeg based driver drowsiness estimation using convolutional neural networks"):
  with members:
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "eeg based user reaction time estimation using riemannian geometry features"
    - "a new generation of brain computer interface based on riemannian geometry"

Connected through hyperedge HE-5 (induced by source paper "active semi supervised transfer learning astl for offline bci calibration"):
  with members:
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "online and offline domain adaptation for reducing bci calibration effort"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"
    - "transfer learning in brain computer interfaces"

Connected through hyperedge HE-6 (induced by source paper "pool based sequential active learning for regression"):
  with members:
    - "feature dimensionality reduction for video affect classification a comparative study"
    - "active learning with statistical models"

Connected through hyperedge HE-7 (induced by source paper "eeg based user reaction time estimation using riemannian geometry features"):
  with members:
    - "riemannian geometry applied to detection of respiratory states from eeg signals the basis for a brain ventilator interface"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "spatial filtering for eeg based regression problems in brain computer interface bci"
    - "transfer learning in brain computer interfaces"
    - "a plug play p300 bci using information geometry"
    - "a new generation of brain computer interface based on riemannian geometry"

Connected through hyperedge HE-8 (induced by source paper "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"):
  with members:
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"
    - "transfer learning in brain computer interfaces"
    - "brain computer interface technologies in the coming decades"

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)