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: "supersaliency predicting smooth pursuit based attention with slicing cnns improves fixation prediction for naturalistic videos"
  Abstract: Predicting attention is a popular topic at the intersection of human and computer vision, but video saliency prediction has only recently begun to benefit from deep learning-based approaches. Even though most of the available video-based saliency data sets and models claim to target human observers' fixations, they fail to differentiate them from smooth pursuit (SP), a major eye movement type that is unique to perception of dynamic scenes. In this work, we aim to make this distinction explicit, to which end we (i) use both algorithmic and manual annotations of SP traces and other eye movements for two well-established video saliency data sets, (ii) train Slicing Convolutional Neural Networks (S-CNN) for saliency prediction on either fixation- or SP-salient locations, and (iii) evaluate ours and over 20 popular published saliency models on the two annotated data sets for predicting both SP and fixations, as well as on another data set of human fixations. Our proposed model, trained on an independent set of videos, outperforms the state-of-the-art saliency models in the task of SP prediction on all considered data sets. Moreover, this model also demonstrates superior performance in the prediction of "classical" fixation-based saliency. Our results emphasize the importance of selectively approaching training set construction for attention modelling.

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)