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: "active semi supervised transfer learning astl for offline bci calibration"
  Abstract: Single-trial classification of event-related potentials in electroencephalogram (EEG) signals is a very important paradigm of brain-computer interface (BCI). Because of individual differences, usually some subject-specific calibration data are required to tailor the classifier for each subject. Transfer learning has been extensively used to reduce such calibration data requirement, by making use of auxiliary data from similar/relevant subjects/tasks. However, all previous research assumes that all auxiliary data have been labeled. This paper considers a more general scenario, in which part of the auxiliary data could be unlabeled. We propose active semi-supervised transfer learning (ASTL) for offline BCI calibration, which integrates active learning, semi-supervised learning, and transfer learning. Using a visual evoked potential oddball task and three different EEG headsets, we demonstrate that ASTL can achieve consistently good performance across subjects and headsets, and it outperforms some state-of-the-art approaches in the literature.

Connected through hyperedge HE-1 (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"
    - "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"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"

Connected through hyperedge HE-2 (induced by source paper "transfer learning for brain computer interfaces an euclidean space data alignment approach"):
  with members:
    - "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"
    - "a plug play p300 bci using information geometry"

Connected through hyperedge HE-3 (induced by source paper "active learning for regression using greedy sampling"):
  with members:
    - "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression"
    - "pool based sequential active learning for regression"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"

Connected through hyperedge HE-4 (induced by source paper "affect estimation in 3d space using multi task active learning for regression"):
  with members:
    - "active learning for regression using greedy sampling"
    - "pool based sequential active learning for regression"
    - "a survey on multi task learning"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"

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)