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: "transfer learning in brain computer interfaces"
  Abstract: The performance of brain-computer interfaces (BCIs) improves with the amount of available training data, the statistical distribution of this data, however, varies across subjects as well as across sessions within individual subjects, limiting the transferability of training data or trained models between them. In this article, we review current transfer learning techniques in BCIs that exploit shared structure between training data of multiple subjects and/or sessions to increase performance. We then present a framework for transfer learning in the context of BCIs that can be applied to any arbitrary feature space, as well as a novel regression estimation method that is specifically designed for the structure of a system based on the electroencephalogram (EEG). We demonstrate the utility of our framework and method on subject-to-subject transfer in a motor-imagery paradigm as well as on session-to-session transfer in one patient diagnosed with amyotrophic lateral sclerosis (ALS), showing that it is able to outperform other comparable methods on an identical dataset.

Connected through hyperedge HE-1 (induced by source paper "cross subject decoding of eye movement goals from local field potentials"):
  with members:
    - "minimax optimal decoding of movement goals from local field potentials using complex spectral features"
    - "meta learning a survey"
    - "return of frustratingly easy domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "transfer learning enhanced common spatial pattern filtering for brain computer interfaces bcis overview and a new approach"):
  with members:
    - "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"
    - "brain computer interface technologies in the coming decades"

Connected through hyperedge HE-3 (induced by source paper "active semi supervised transfer learning astl for offline bci calibration"):
  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"
    - "online and offline domain adaptation for reducing bci calibration effort"
    - "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 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"
    - "a plug play p300 bci using information geometry"
    - "a new generation of brain computer interface based on riemannian geometry"

Connected through hyperedge HE-5 (induced by source paper "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"):
  with members:
    - "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"
    - "brain computer interface technologies in the coming decades"

Connected through hyperedge HE-6 (induced by source paper "online and offline domain adaptation for reducing bci calibration effort"):
  with members:
    - "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"
    - "class imbalance problem in data mining review"

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)