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: "sg fcn a motion and memory based deep learning model for video saliency detection"
  Abstract: Data-driven saliency detection has attracted strong interest as a result of applying convolutional neural networks to the detection of eye fixations. Although a number of image-based salient object and fixation detection models have been proposed, video fixation detection still requires more exploration. Different from image analysis, motion and temporal information is a crucial factor affecting human attention when viewing video sequences. Although existing models based on local contrast and low-level features have been extensively researched, they failed to simultaneously consider interframe motion and temporal information across neighboring video frames, leading to unsatisfactory performance when handling complex scenes. To this end, we propose a novel and efficient video eye fixation detection model to improve the saliency detection performance. By simulating the memory mechanism and visual attention mechanism of human beings when watching a video, we propose a step-gained fully convolutional network by combining the memory information on the time axis with the motion information on the space axis while storing the saliency information of the current frame. The model is obtained through hierarchical training, which ensures the accuracy of the detection. Extensive experiments in comparison with 11 state-of-the-art methods are carried out, and the results show that our proposed model outperforms all 11 methods across a number of publicly available datasets.

Connected through hyperedge HE-1 (induced by source paper "depthwise non local module for fast salient object detection using a single thread"):
  with members:
    - "motion guided attention for video salient object detection"
    - "a simple pooling based design for real time salient object detection"
    - "non local context encoder robust biomedical image segmentation against adversarial attacks"
    - "rosa robust salient object detection against adversarial attacks"
    - "deeply supervised salient object detection with short connections"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "contrast oriented deep neural networks for salient object detection"
    - "non locally enhanced encoder decoder network for single image de raining"

Connected through hyperedge HE-2 (induced by source paper "minor constraint disturbances for deep semi supervised learning"):
  with members:
    - "semi supervised semantic segmentation with high and low level consistency"
    - "generalized matrix means for semi supervised learning with multilayer graphs"
    - "stacking based deep neural network deep analytic network for pattern classification"
    - "a novel predictive coding inspired variational rnn model for online prediction and recognition"
    - "restricted boltzmann machines with gaussian visible units guided by pairwise constraints"

Connected through hyperedge HE-3 (induced by source paper "synthetic defocus and look ahead autofocus for casual videography"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "synthetic depth of field with a single camera mobile phone"
    - "deeplens shallow depth of field from a single image"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "scanner efficient video analysis at scale"
    - "tracking emerges by colorizing videos"
    - "intel realsense stereoscopic depth cameras"

Connected through hyperedge HE-4 (induced by source paper "motion guided attention for video salient object detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "video salient object detection using spatiotemporal deep features"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "video salient object detection via fully convolutional networks"
    - "reverse attention for salient object detection"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "learning uncertain convolutional features for accurate saliency detection"

Connected through hyperedge HE-5 (induced by source paper "saliency prediction in the deep learning era successes limitations and future challenges"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "deep visual attention prediction"
    - "charades ego a large scale dataset of paired third and first person videos"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "learning a saliency evaluation metric using crowdsourced perceptual judgments"
    - "deepvs a deep learning based video saliency prediction approach"
    - "pros and cons of gan evaluation measures"

Connected through hyperedge HE-6 (induced by source paper "bottom up attention models of"):
  with members:
    - "understanding and visualizing deep visual saliency models"
    - "charades ego a large scale dataset of paired third and first person videos"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "eml net an expandable multi layer network for saliency prediction"
    - "saliency benchmarking made easy separating models maps and metrics"

Connected through hyperedge HE-7 (induced by source paper "global and local sensitivity guided key salient object re augmentation for video saliency detection"):
  with members:
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "video salient object detection via fully convolutional networks"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "exploiting saliency for object segmentation from image level labels"
    - "supervising neural attention models for video captioning by human gaze data"
    - "deepsaliency multi task deep neural network model for salient object detection"

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)