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 "different set domain adaptation for brain computer interfaces a label alignment approach"):
  Abstract: A brain-computer interface (BCI) system usually needs a long calibration session for each new subject/task to adjust its parameters, which impedes its transition from the laboratory to real-world applications. Domain adaptation, which leverages labeled data from auxiliary subjects/tasks (source domains), has demonstrated its effectiveness in reducing such calibration effort. Currently, most domain adaptation approaches require the source domains to have the same feature space and label space as the target domain, which limits their applications, as the auxiliary data may have different feature spaces and/or different label spaces. This paper considers different set domain adaptation for BCIs, i.e., the source and target domains have different label spaces. We introduce a practical setting of different label sets for BCIs, and propose a novel label alignment (LA) approach to align the source label space with the target label space. It has three desirable properties: 1) LA only needs as few as one labeled sample from each class of the target subject; 2) LA can be used as a preprocessing step before different feature extraction and classification algorithms; and, 3) LA can be integrated with other domain adaptation approaches to achieve even better performance. Experiments on two motor imagery datasets demonstrated the effectiveness of LA.

Includes members:
  Member "open set domain adaptation theoretical bound and algorithm":
    also connected through other hyperedges:
      (none)
  Member "open set domain adaptation by backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "active semi supervised transfer learning astl for offline bci calibration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transfer learning for brain computer interfaces an euclidean space data alignment approach")
  Member "visual domain adaptation with manifold embedded distribution alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "impact of imagenet model selection on domain adaptation")
  Member "transfer learning enhanced common spatial pattern filtering for brain computer interfaces bcis overview and a new approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transfer learning for brain computer interfaces an euclidean space data alignment approach")
  Member "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr":
    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 "online and offline domain adaptation for reducing bci calibration effort":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ultra efficient transfer learning with meta update for cross subject eeg classification")
  Member "joint geometrical and statistical alignment for visual domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "domain adaptive few shot learning")

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)