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: "a feasibility study on ssvep based interaction with motivating and immersive virtual and augmented reality"
  Abstract: Non-invasive steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems offer high bandwidth compared to other BCI types and require only minimal calibration and training. Virtual reality (VR) has been already validated as effective, safe, affordable and motivating feedback modality for BCI experiments. Augmented reality (AR) enhances the physical world by superimposing informative, context sensitive, computer generated content. In the context of BCI, AR can be used as a friendlier and more intuitive real-world user interface, thereby facilitating a more seamless and goal directed interaction. This can improve practicality and usability of BCI systems and may help to compensate for their low bandwidth. In this feasibility study, three healthy participants had to finish a complex navigation task in immersive VR and AR conditions using an online SSVEP BCI. Two out of three subjects were successful in all conditions. To our knowledge, this is the first work to present an SSVEP BCI that operates using target stimuli integrated in immersive VR and AR (head-mounted display and camera). This research direction can benefit patients by introducing more intuitive and effective real-world interaction (e.g. smart home control). It may also be relevant for user groups that require or benefit from hands free operation (e.g. due to temporary situational disability).

Connected through hyperedge HE-1 (induced by source paper "compact convolutional neural networks for classification of asynchronous steady state visual evoked potentials"):
  with members:
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "learning robust features using deep learning for automatic seizure detection"
    - "xception deep learning with depthwise separable convolutions"
    - "tensorflow a system for large scale machine learning"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"

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)