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: "guiding human gaze with convolutional neural networks"
  Abstract: The eye fixation patterns of human observers are a fundamental indicator of the aspects of an image to which humans attend. Thus, manipulating fixation patterns to guide human attention is an exciting challenge in digital image processing. Here, we present a new model for manipulating images to change the distribution of human fixations in a controlled fashion. We use the state-of-the-art model for fixation prediction to train a convolutional neural network to transform images so that they satisfy a given fixation distribution. For network training, we carefully design a loss function to achieve a perceptual effect while preserving naturalness of the transformed images. Finally, we evaluate the success of our model by measuring human fixations for a set of manipulated images. On our test images we can in-/decrease the probability to fixate on selected objects on average by 43/22% but show that the effectiveness of the model depends on the semantic content of the manipulated images.

Connected through hyperedge HE-1 (induced by source paper "saliency prediction in the deep learning era successes limitations and future challenges"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "deep visual attention prediction"
    - "charades ego a large scale dataset of paired third and first person videos"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "learning a saliency evaluation metric using crowdsourced perceptual judgments"
    - "deepvs a deep learning based video saliency prediction approach"

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)