human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "eeg based user reaction time estimation using riemannian geometry features"):
  Abstract: Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first time, it is applied to BCI regression problems, an important category of BCI applications. More specifically, we propose a new feature extraction approach for Electroencephalogram (EEG) based BCI regression problems: a spatial filter is first used to increase the signal quality of the EEG trials and also to reduce the dimensionality of the covariance matrices, and then Riemannian tangent space features are extracted. We validate the performance of the proposed approach in reaction time estimation from EEG signals measured in a large-scale sustained-attention psychomotor vigilance task, and show that compared with the traditional powerband features, the tangent space features can reduce the root mean square estimation error by 4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.

Includes members:
  Member "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised pool based active learning for linear regression")
  Member "riemannian geometry applied to detection of respiratory states from eeg signals the basis for a brain ventilator interface":
    also connected through other hyperedges:
      (none)
  Member "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ultra efficient transfer learning with meta update for cross subject eeg classification")
  Member "spatial filtering for eeg based regression problems in brain computer interface bci":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the vulnerability of cnn classifiers in eeg based bcis")
  Member "transfer learning in brain computer interfaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross subject decoding of eye movement goals from local field potentials")
  Member "a plug play p300 bci using information geometry":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning user preferences for trajectories from brain signals")
  Member "a new generation of brain computer interface based on riemannian geometry":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "building brain invaders eeg data of an experimental validation")

Question: Please tell me which class the source paper 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)