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 "development of a game with a purpose for acquisition of brain computer interface data"):
  Abstract: Brain-computer interfaces (BCIs) have the potential to significantly change the ways in which humans interact with technology, the environment, and even each other. Unfortunately, BCI technologies are seldom robust enough for use in real-world applications, in part due to the large amount of data that must be collected, processed, and classified in order to develop models of task-related neural activity that account for two of the most important and least-understood drivers of BCI illiteracy: individual differences in neural signals and intra-individual differences across interdependent, time-varying neural states. This paper describes the feasibility of using a game with a purpose (GWAP) as a viable instrument for collecting data from BCI-relevant research tasks. By leveraging game-related reward processes to maintain participant interest and engagement, this approach will enable large amounts of BCI data to be acquired, both across many individuals and longitudinally from specific individuals as neural states vary naturally over time. Pilot and technical testing results are presented here to demonstrate that the BCI-relevant tasks embedded within the research game elicit neural signals similar to those that would be expected from more traditional BCI tasks. These preliminary data provide support and validation of the use of GWAPs as promising tools to enable long-term collection of BCI-relevant data in an engaging environment.

Includes members:
  Member "eegnet a compact convolutional neural network for eeg based brain computer interfaces":
    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 "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ultra efficient transfer learning with meta update for cross subject eeg classification")
  Member "brain computer interface technologies in the coming decades":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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)