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: "a neurodynamic model of saliency prediction in v1"
  Abstract: Lateral connections in the primary visual cortex (area V1 or striate cortex) have long been hypothesized to be responsible of several visual processing mechanisms such as brightness induction, chromatic induction, visual discomfort and bottom-up visual attention (also named saliency). Many computational models have been developed to independently predict these and other visual processes, but no computational model has been able to reproduce all of them simultaneously. In this work we show that a biologically plausible computational model of lateral interactions of V1 is able to simultaneously predict saliency and all the aforementioned visual processes. Our model's (named Neurodynamic Saliency WAvelet Model or NSWAM) architecture is based on Pennachio's neurodynamic model of lateral connections of V1 (defined as a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation and scale). We tested NSWAM saliency predictions using images from eye tracking datasets, showing that it is an improvement with respect to previous models as well as consistent with human psychophysics. Hence, we show that our biologically plausible model of lateral connections can simultaneously explain different visual proceses present in V1 (without applying any type of training or optimization and keeping the same parametrization for all the visual processes). This can be useful for the definition of a unified architecture of the primary visual cortex.

Connected through hyperedge HE-1 (induced by source paper "sid4vam a benchmark dataset with synthetic images for visual attention modeling"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "psychophysical evaluation of individual low level feature influences on visual attention"
    - "the elephant in the room"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "a deep multi level network for saliency prediction"
    - "what do different evaluation metrics tell us about saliency models"
    - "opensalicon an open source implementation of the salicon saliency model"
    - "cat2000 a large scale fixation dataset for boosting saliency research"

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)