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: "mixing board versus mouse interaction in value adjustment tasks"
  Abstract: We present a controlled, quantitative study with 12 participants comparing interaction with a haptically enhanced mixing board against interaction with a mouse in an abstract task that is motivated by several practical parameter space exploration settings. #R##N#The study participants received 24 sets of one to eight integer values between 0 and 127, which they had to match by making adjustments with physical or graphical sliders. Based on recorded slider motion path data, we developed an analysis algorithm that identifies and measures different types of activity intervals, including error time moving irrelevant sliders and end time in breaks after completing each trial item. Our results showed a significant increase in speed of the mixing board interaction accompanied by reduced perceived cognitive load when compared with the traditional mouse-based GUI interaction. The gains in speed are largely due to the improved times required for the hand to reach for the first slider (acquisition time) and also when moving between different ones, while the actual time spent manipulating relevant sliders is very similar for either input device. These results agree strongly with qualitative predictions from Fitts' Law that the larger targets afforded by the mixer handles contributed to its faster performance. For further investigation, we computed a measure of motion simultaneity based on velocity correlation, which allowed to identify types of items for which increased simultaneous adjustments occur. #R##N#For continuous parameter space exploration our findings suggest that mixing boards are a good option to provide detailed multi-value control. The strengths of this input method particularly show in settings where screen space is precious and undisrupted visual focus is crucial.

Connected through hyperedge HE-1 (induced by source paper "paraglide interactive parameter space partitioning for computer simulations"):
  with members:
    - "a tutorial on spectral clustering"

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)