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: "end to end convolutional network for saliency prediction"
  Abstract: The prediction of saliency areas in images has been traditionally addressed with hand crafted features based on neuroscience principles. This paper however addresses the problem with a completely data-driven approach by training a convolutional network. The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency prediction has provided enough data to train a not very deep architecture which is both fast and accurate. The convolutional network in this paper, named JuntingNet, won the LSUN 2015 challenge on saliency prediction with a superior performance in all considered metrics.

Connected through hyperedge HE-1 (induced by source paper "saliency difference based objective evaluation method for a superimposed screen of the hud with various background"):
  with members:
    - "visual saliency estimation by integrating features using multiple kernel learning"

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"
    - "reconciling saliency and object center bias hypotheses in explaining free viewing fixations"
    - "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"
    - "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 "deepfeat a bottom up and top down saliency model based on deep features of convolutional neural nets"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "end to end saliency mapping via probability distribution prediction"
    - "visualizing residual networks"
    - "neural networks based eeg speech models"
    - "a deep multi level network for saliency prediction"
    - "deepgaze ii reading fixations from deep features trained on object recognition"
    - "a deep spatial contextual long term recurrent convolutional network for saliency detection"
    - "what do different evaluation metrics tell us about saliency models"

Connected through hyperedge HE-4 (induced by source paper "semantic summarization of egocentric photo stream events"):
  with members:
    - "r clustering for egocentric video segmentation"
    - "toward storytelling from visual lifelogging an overview"
    - "multi face tracking by extended bag of tracklets in egocentric photo streams"
    - "ego object discovery"
    - "visual summary of egocentric photostreams by representative keyframes"
    - "how transferable are features in deep neural networks"
    - "lsda large scale detection through adaptation"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "recurrent mixture density network for spatiotemporal visual attention"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "deep learning for saliency prediction in natural video"
    - "visual saliency based on multiscale deep features"
    - "beyond short snippets deep networks for video classification"
    - "attentionnet aggregating weak directions for accurate object detection"
    - "action recognition using visual attention"
    - "draw a recurrent neural network for image generation"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "wepsam weakly pre learnt saliency model"):
  with members:
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "visual language modeling on cnn image representations"
    - "turkergaze crowdsourcing saliency with webcam based eye tracking"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "visual saliency estimation by integrating features using multiple kernel learning"

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)