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: "brain computer interface technologies in the coming decades"
  Abstract: As the proliferation of technology dramatically infiltrates all aspects of modern life, in many ways the world is becoming so dynamic and complex that technological capabilities are overwhelming human capabilities to optimally interact with and leverage those technologies. Fortunately, these technological advancements have also driven an explosion of neuroscience research over the past several decades, presenting engineers with a remarkable opportunity to design and develop flexible and adaptive brain-based neurotechnologies that integrate with and capitalize on human capabilities and limitations to improve human-system interactions. Major forerunners of this conception are brain-computer interfaces (BCIs), which to this point have been largely focused on improving the quality of life for particular clinical populations and include, for example, applications for advanced communications with paralyzed or “locked in” patients as well as the direct control of prostheses and wheelchairs. Near-term applications are envisioned that are primarily task oriented and are targeted to avoid the most difficult obstacles to development. In the farther term, a holistic approach to BCIs will enable a broad range of task-oriented and opportunistic applications by leveraging pervasive technologies and advanced analytical approaches to sense and merge critical brain, behavioral, task, and environmental information. Communications and other applications that are envisioned to be broadly impacted by BCIs are highlighted; however, these represent just a small sample of the potential of these technologies.

Connected through hyperedge HE-1 (induced by source paper "white box target attack for eeg based bci regression problems"):
  with members:
    - "on the vulnerability of cnn classifiers in eeg based bcis"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "eeg based user reaction time estimation using riemannian geometry features"
    - "spatial filtering for eeg based regression problems in brain computer interface bci"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-2 (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"
    - "active semi supervised transfer learning astl for offline bci calibration"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "development of a game with a purpose for acquisition of brain computer interface data"):
  with members:
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"

Connected through hyperedge HE-4 (induced by source paper "eegnet a compact convolutional neural network for eeg based brain computer interfaces"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "learning robust features using deep learning for automatic seizure detection"
    - "why should i trust you explaining the predictions of any classifier"
    - "xception deep learning with depthwise separable convolutions"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "transfer learning for brain computer interfaces an euclidean space data alignment approach"):
  with members:
    - "active semi supervised transfer learning astl for offline bci calibration"
    - "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"

Connected through hyperedge HE-6 (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"
    - "transfer learning in brain computer interfaces"

Connected through hyperedge HE-7 (induced by source paper "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression"):
  with members:
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"
    - "active learning with statistical models"

Connected through hyperedge HE-8 (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"
    - "transfer learning in brain computer interfaces"

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)