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: "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"
  Abstract: This paper presents a novel fixation prediction and saliency modeling framework based on inter-image similarities and ensemble of Extreme Learning Machines (ELM). The proposed framework is inspired by two observations, (1) the contextual information of a scene along with low-level visual cues modulates attention, (2) the influence of scene memorability on eye movement patterns caused by the resemblance of a scene to a former visual experience. Motivated by such observations, we develop a framework that estimates the saliency of a given image using an ensemble of extreme learners, each trained on an image similar to the input image. That is, after retrieving a set of similar images for a given image, a saliency predictor is learnt from each of the images in the retrieved image set using an ELM, resulting in an ensemble. The saliency of the given image is then measured in terms of the mean of predicted saliency value by the ensembles members.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "adversarial spatio temporal learning for video deblurring"
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "video salient object detection via fully convolutional networks"
    - "how drones look crowdsourced knowledge transfer for aerial video saliency prediction"
    - "deep mutual learning"
    - "using deep networks for drone detection"

Connected through hyperedge HE-2 (induced by source paper "saliency for fine grained object recognition in domains with scarce training data"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "deep visual attention prediction"
    - "feature space transfer for data augmentation"
    - "dynamic few shot visual learning without forgetting"
    - "deep layer aggregation"
    - "prototypical networks for few shot learning"
    - "fine grained categorization via cnn based automatic extraction and integration of object level and part level features"
    - "paying more attention to saliency image captioning with saliency and context attention"

Connected through hyperedge HE-3 (induced by source paper "deep saliency models the quest for the loss function"):
  with members:
    - "understanding and visualizing deep visual saliency models"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "eml net an expandable multi layer network for saliency prediction"
    - "an element sensitive saliency model with position prior learning for web pages"
    - "end to end saliency mapping via probability distribution prediction"
    - "video salient object detection via fully convolutional networks"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-4 (induced by source paper "ultrafast video attention prediction with coupled knowledge distillation"):
  with members:
    - "deep visual attention prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deepvs a deep learning based video saliency prediction approach"
    - "how drones look crowdsourced knowledge transfer for aerial video saliency prediction"
    - "cbam convolutional block attention module"
    - "deep mutual learning"
    - "spatio temporal saliency networks for dynamic saliency prediction"
    - "deepgaze ii reading fixations from deep features trained on object recognition"

Connected through hyperedge HE-5 (induced by source paper "sg fcn a motion and memory based deep learning model for video saliency detection"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "exploring models and data for remote sensing image caption generation"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "geometric hypergraph learning for visual tracking"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "adaptive compressive tracking via online vector boosting feature selection"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "saliency prediction in the deep learning era successes limitations and future challenges"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "predicting human eye fixations via an lstm based saliency attentive model"):
  with members:
    - "visual saliency prediction using a mixture of deep neural networks"
    - "end to end saliency mapping via probability distribution prediction"
    - "attention is all you need"
    - "paying more attention to saliency image captioning with saliency and context attention"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "a deep multi level network for saliency prediction"
    - "seeing with humans gaze assisted neural image captioning"

Connected through hyperedge HE-8 (induced by source paper "eml net an expandable multi layer network for saliency prediction"):
  with members:
    - "deep visual attention prediction"
    - "influence of image classification accuracy on saliency map estimation"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "end to end saliency mapping via probability distribution prediction"
    - "learning transferable architectures for scalable image recognition"
    - "a deep multi level network for saliency prediction"
    - "densely connected convolutional networks"
    - "a deep spatial contextual long term recurrent convolutional network for saliency 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)