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: "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"
  Abstract: Electroencephalography (EEG) headsets are the most commonly used sensing devices for brain–computer interface. In real-world applications, there are advantages to extrapolating data from one user session to another. However, these advantages are limited if the data arise from different hardware systems, which often vary between application spaces. Currently, this creates a need to recalibrate classifiers, which negatively affects people's interest in using such systems. In this paper, we employ active weighted adaptation regularization (AwAR), which integrates weighted adaptation regularization (wAR) and active learning, to expedite the calibration process. wAR makes use of labeled data from the previous headset and handles class-imbalance, and active learning selects the most informative samples from the new headset to label. Experiments on single-trial event-related potential classification show that AwAR can significantly increase the classification accuracy, given the same number of labeled samples from the new headset. In other words, AwAR can effectively reduce the number of labeled samples required from the new headset, given a desired classification accuracy, suggesting value in collating data for use in wide scale transfer-learning applications.

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"
    - "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"
    - "matching networks for one shot learning"

Connected through hyperedge HE-2 (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"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "eeg based user reaction time estimation using riemannian geometry features"

Connected through hyperedge HE-3 (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"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"

Connected through hyperedge HE-4 (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"
    - "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "discriminative joint probability maximum mean discrepancy djp mmd for domain adaptation"):
  with members:
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "supervised transfer learning for product information question answering"
    - "balanced distribution adaptation for transfer learning"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "online and offline domain adaptation for reducing bci calibration effort"
    - "joint geometrical and statistical alignment for visual domain adaptation"

Connected through hyperedge HE-6 (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"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "online and offline domain adaptation for reducing bci calibration effort"
    - "joint geometrical and statistical alignment for visual domain adaptation"

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"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "online and offline domain adaptation for reducing bci calibration effort"
    - "joint geometrical and statistical alignment for visual domain adaptation"
    - "return of frustratingly easy domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "active stacking for heart rate estimation"):
  with members:
    - "active learning for regression using greedy sampling"
    - "affect estimation in 3d space using multi task active learning for regression"

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)