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: "a plug play p300 bci using information geometry"
  Abstract: This paper presents a new classification methods for Event Related Potentials (ERP) based on an Information geometry framework. Through a new estimation of covariance matrices, this work extend the use of Riemannian geometry, which was previously limited to SMR-based BCI, to the problem of classification of ERPs. As compared to the state-of-the-art, this new method increases performance, reduces the number of data needed for the calibration and features good generalisation across sessions and subjects. This method is illustrated on data recorded with the P300-based game brain invaders. Finally, an online and adaptive implementation is described, where the BCI is initialized with generic parameters derived from a database and continuously adapt to the individual, allowing the user to play the game without any calibration while keeping a high accuracy.

Connected through hyperedge HE-1 (induced by source paper "learning user preferences for trajectories from brain signals"):
  with members:
    - "configuration space metrics"
    - "acting thoughts towards a mobile robotic service assistant for users with limited communication skills"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "simple robust and optimal ranking from pairwise comparisons"

Connected through hyperedge HE-2 (induced by source paper "optimal transport on the manifold of spd matrices for domain adaptation"):
  with members:
    - "optimal transport for domain adaptation"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "domain adaptation for statistical classifiers"

Connected through hyperedge HE-3 (induced by source paper "building brain invaders eeg data of an experimental validation"):
  with members:
    - "moabb trustworthy algorithm benchmarking for bcis"
    - "a new generation of brain computer interface based on riemannian geometry"

Connected through hyperedge HE-4 (induced by source paper "brain invaders adaptive versus non adaptive p300 brain computer interface dataset"):
  with members:
    - "moabb trustworthy algorithm benchmarking for bcis"
    - "a new generation of brain computer interface based on riemannian geometry"

Connected through hyperedge HE-5 (induced by source paper "systematic enhancement of functional connectivity in brain computer interfacing using common spatial patterns and tangent space mapping"):
  with members:
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"

Connected through hyperedge HE-6 (induced by source paper "eegnet a compact convolutional neural network for eeg based brain computer interfaces"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "learning robust features using deep learning for automatic seizure detection"
    - "why should i trust you explaining the predictions of any classifier"
    - "xception deep learning with depthwise separable convolutions"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-7 (induced by source paper "transfer learning for brain computer interfaces an euclidean space data alignment approach"):
  with members:
    - "active semi supervised transfer learning astl for offline bci calibration"
    - "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"
    - "eeg based user reaction time estimation using riemannian geometry features"
    - "online and offline domain adaptation for reducing bci calibration effort"
    - "spatial filtering for eeg based regression problems in brain computer interface bci"
    - "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 "eeg based user reaction time estimation using riemannian geometry features"):
  with members:
    - "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression"
    - "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 new generation of brain computer interface based on riemannian geometry"

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)