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 new generation of brain computer interface based on riemannian geometry"
  Abstract: Based on the cumulated experience over the past 25 years in the field of Brain-Computer Interface (BCI) we can now envision a new generation of BCI. Such BCIs will not require training; instead they will be smartly initialized using remote massive databases and will adapt to the user fast and effectively in the first minute of use. They will be reliable, robust and will maintain good performances within and across sessions. A general classification framework based on recent advances in Riemannian geometry and possessing these characteristics is presented. It applies equally well to BCI based on event-related potentials (ERP), sensorimotor (mu) rhythms and steady-state evoked potential (SSEP). The framework is very simple, both algorithmically and computationally. Due to its simplicity, its ability to learn rapidly (with little training data) and its good across-subject and across-session generalization, this strategy a very good candidate for building a new generation of BCIs, thus we hereby propose it as a benchmark method for the field.

Connected through hyperedge HE-1 (induced by source paper "building brain invaders eeg data of an experimental validation"):
  with members:
    - "moabb trustworthy algorithm benchmarking for bcis"
    - "a plug play p300 bci using information geometry"

Connected through hyperedge HE-2 (induced by source paper "brain invaders adaptive versus non adaptive p300 brain computer interface dataset"):
  with members:
    - "moabb trustworthy algorithm benchmarking for bcis"
    - "a plug play p300 bci using information geometry"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "eeg based driver drowsiness estimation using convolutional neural networks"):
  with members:
    - "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression"
    - "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"

Connected through hyperedge HE-6 (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 plug play p300 bci using information geometry"

Connected through hyperedge HE-7 (induced by source paper "spatial filtering for eeg based regression problems in brain computer interface bci"):
  with members:
    - "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"

Connected through hyperedge HE-8 (induced by source paper "using riemannian geometry for ssvep based brain computer interface"):
  with members:
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"

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.HC(Human-Computer Interaction)