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: "reconciling saliency and object center bias hypotheses in explaining free viewing fixations"
  Abstract: Predicting where people look in natural scenes has attracted a lot of interest in computer vision and computational neuroscience over the past two decades. Two seemingly contrasting categories of cues have been proposed to influence where people look: \textit{low-level image saliency} and \textit{high-level semantic information}. Our first contribution is to take a detailed look at these cues to confirm the hypothesis proposed by Henderson~\cite{henderson1993eye} and Nuthmann \& Henderson~\cite{nuthmann2010object} that observers tend to look at the center of objects. We analyzed fixation data for scene free-viewing over 17 observers on 60 fully annotated images with various types of objects. Images contained different types of scenes, such as natural scenes, line drawings, and 3D rendered scenes. Our second contribution is to propose a simple combined model of low-level saliency and object center-bias that outperforms each individual component significantly over our data, as well as on the OSIE dataset by Xu et al.~\cite{xu2014predicting}. The results reconcile saliency with object center-bias hypotheses and highlight that both types of cues are important in guiding fixations. Our work opens new directions to understand strategies that humans use in observing scenes and objects, and demonstrates the construction of combined models of low-level saliency and high-level object-based information.

Connected through hyperedge HE-1 (induced by source paper "psychophysical evaluation of individual low level feature influences on visual attention"):
  with members:
    - "saccade sequence prediction beyond static saliency maps"
    - "cat2000 a large scale fixation dataset for boosting saliency research"
    - "how close are we to understanding image based saliency"

Connected through hyperedge HE-2 (induced by source paper "end to end saliency mapping via probability distribution prediction"):
  with members:
    - "pet an eye tracking dataset for animal centric pascal object classes"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "turkergaze crowdsourcing saliency with webcam based eye tracking"
    - "end to end convolutional network for saliency prediction"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "deep gaze i boosting saliency prediction with feature maps trained on imagenet"

Connected through hyperedge HE-3 (induced by source paper "influence of image classification accuracy on saliency map estimation"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "memory efficient implementation of densenets"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deepgaze ii reading fixations from deep features trained on object recognition"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a deep spatial contextual long term recurrent convolutional network for saliency detection"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"

Connected through hyperedge HE-4 (induced by source paper "personrank detecting important people in images"):
  with members:
    - "ssd single shot multibox detector"
    - "with whom do i interact detecting social interactions in egocentric photo streams"
    - "human social interaction modeling using temporal deep networks"
    - "vip finding important people in images"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "detecting events and key actors in multi person videos"
    - "predicting important objects for egocentric video summarization"

Connected through hyperedge HE-5 (induced by source paper "salient object detection a benchmark"):
  with members:
    - "salient object detection a survey"
    - "salient object detection a discriminative regional feature integration approach"
    - "salient object subitizing"
    - "visual saliency based on multiscale deep features"
    - "vqa visual question answering"
    - "what is a salient object a dataset and a baseline model for salient object detection"
    - "microsoft coco captions data collection and evaluation server"
    - "going deeper with convolutions"

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)