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 "transfer learning enhanced common spatial pattern filtering for brain computer interfaces bcis overview and a new approach"):
  Abstract: The electroencephalogram (EEG) is the most widely used input for brain computer interfaces (BCIs), and common spatial pattern (CSP) is frequently used to spatially filter it to increase its signal-to-noise ratio. However, CSP is a supervised filter, which needs some subject-specific calibration data to design. This is time-consuming and not user-friendly. A promising approach for shortening or even completely eliminating this calibration session is transfer learning, which leverages relevant data or knowledge from other subjects or tasks. This paper reviews three existing approaches for incorporating transfer learning into CSP, and also proposes a new transfer learning enhanced CSP approach. Experiments on motor imagery classification demonstrate their effectiveness. Particularly, our proposed approach achieves the best performance when the number of target domain calibration samples is small.

Includes members:
  Member "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ultra efficient transfer learning with meta update for cross subject eeg classification")
  Member "eeg based user reaction time estimation using riemannian geometry features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the vulnerability of cnn classifiers in eeg based bcis")
  Member "online and offline domain adaptation for reducing bci calibration effort":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "on the vulnerability of cnn classifiers in eeg based bcis")
  Member "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ultra efficient transfer learning with meta update for cross subject eeg classification")
  Member "transfer learning in brain computer interfaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cross subject decoding of eye movement goals from local field potentials")
  Member "brain computer interface technologies in the coming decades":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "white box target attack for eeg based bci regression problems")

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)