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: "the effect of color space selection on detectability and discriminability of colored objects"
  Abstract: In this paper, we investigate the effect of color space selection on detectability and discriminability of colored objects under various conditions. 20 color spaces from the literature are evaluated on a large dataset of simulated and real images. We measure the suitability of color spaces from two different perspectives: detectability and discriminability of various color groups. Through experimental evaluation, we found that there is no single optimal color space suitable for all color groups. The color spaces have different levels of sensitivity to different color groups and they are useful depending on the color of the sought object. Overall, the best results were achieved in both simulated and real images using color spaces C1C2C3, UVW and XYZ. In addition, using a simulated environment, we show a practical application of color space selection in the context of top-down control in active visual search. The results indicate that on average color space C1C2C3 followed by HSI and XYZ achieve the best time in searching for objects of various colors. Here, the right choice of color space can improve time of search on average by 20%. As part of our contribution, we also introduce a large dataset of simulated 3D objects

Connected through hyperedge HE-1 (induced by source paper "using an ensemble color space model to tackle adversarial examples"):
  with members:
    - "colornet investigating the importance of color spaces for image classification"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "divide denoise and defend against adversarial attacks"
    - "deflecting adversarial attacks with pixel deflection"
    - "certified defenses against adversarial examples"
    - "magnet a two pronged defense against adversarial examples"
    - "ape gan adversarial perturbation elimination with gan"
    - "countering adversarial images using input transformations"

Connected through hyperedge HE-2 (induced by source paper "hypermap mapping framework and its application to autonomous semantic exploration"):
  with members:
    - "yolact real time instance segmentation"
    - "deep network uncertainty maps for indoor navigation"
    - "planning for robotic exploration based on forward simulation"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-3 (induced by source paper "attention based active visual search for mobile robots"):
  with members:
    - "revisiting active perception"
    - "visual saliency based on multiscale deep features"

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.CV(Computer Vision and Pattern Recognition)