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: "fixation prediction with a combined model of bottom up saliency and vanishing point"
  Abstract: By predicting where humans look in natural scenes, we can understand how they perceive complex natural scenes and prioritize information for further high-level visual processing. Several models have been proposed for this purpose, yet there is a gap between best existing saliency models and human performance. While many researchers have developed purely computational models for fixation prediction, less attempts have been made to discover cognitive factors that guide gaze. Here, we study the effect of a particular type of scene structural information, known as the vanishing point, and show that human gaze is attracted to the vanishing point regions. We record eye movements of 10 observers over 532 images, out of which 319 have vanishing points. We then construct a combined model of traditional saliency and a vanishing point channel and show that our model outperforms state of the art saliency models using three scores on our dataset.

Connected through hyperedge HE-1 (induced by source paper "saliency integration an arbitrator model"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "hierarchical cellular automata for visual saliency"
    - "salient object detection a discriminative regional feature integration approach"
    - "deep contrast learning for salient object detection"
    - "visual saliency based on multiscale deep features"
    - "salient object detection a benchmark"
    - "a reverse hierarchy model for predicting eye fixations"
    - "visual saliency based on scale space analysis in the frequency domain"

Connected through hyperedge HE-2 (induced by source paper "crowdfix an eyetracking data set of human crowd video"):
  with members:
    - "understanding and visualizing deep visual saliency models"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "actions in the eye dynamic gaze datasets and learnt saliency models for visual recognition"

Connected through hyperedge HE-3 (induced by source paper "how do people explore virtual environments"):
  with members:
    - "movie editing and cognitive event segmentation in virtual reality video"
    - "making 360 circ video watchable in 2d learning videography for click free viewing"
    - "a deep multi level network for saliency prediction"
    - "shallow and deep convolutional networks for saliency prediction"
    - "what do different evaluation metrics tell us about saliency models"
    - "deep learning for saliency prediction in natural video"
    - "learning gaze transitions from depth to improve video saliency estimation"
    - "visual saliency based on multiscale deep features"

Connected through hyperedge HE-4 (induced by source paper "vanishing point detection with convolutional neural networks"):
  with members:
    - "vanishing point attracts gaze in free viewing and visual search tasks"
    - "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)