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: "salient object detection in the deep learning era an in depth survey"
  Abstract: As an important problem in computer vision, salient object detection (SOD) from images has been attracting an increasing amount of research effort over the years. Recent advances in SOD, not surprisingly, are dominantly led by deep learning-based solutions (named deep SOD) and reflected by hundreds of published papers. To facilitate the in-depth understanding of deep SODs, in this paper we provide a comprehensive survey covering various aspects ranging from algorithm taxonomy to unsolved open issues. In particular, we first review deep SOD algorithms from different perspectives including network architecture, level of supervision, learning paradigm and object/instance level detection. Following that, we summarize existing SOD evaluation datasets and metrics. Then, we carefully compile a thorough benchmark results of SOD methods based on previous work, and provide detailed analysis of the comparison results. Moreover, we study the performance of SOD algorithms under different attributes, which have been barely explored previously, by constructing a novel SOD dataset with rich attribute annotations. We further analyze, for the first time in the field, the robustness and transferability of deep SOD models w.r.t. adversarial attacks. We also look into the influence of input perturbations, and the generalization and hardness of existing SOD datasets. Finally, we discuss several open issues and challenges of SOD, and point out possible research directions in future. All the saliency prediction maps, our constructed dataset with annotations, and codes for evaluation are made publicly available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency"):
  with members:
    - "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"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"
    - "using deep networks for drone detection"

Connected through hyperedge HE-2 (induced by source paper "cross layer feature pyramid network for salient object detection"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "a simple pooling based design for real time salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"
    - "reverse attention for salient object detection"
    - "learning uncertain convolutional features for accurate saliency detection"

Connected through hyperedge HE-3 (induced by source paper "unsupervised discovery of interpretable directions in the gan latent space"):
  with members:
    - "high fidelity synthesis with disentangled representation"
    - "semantic hierarchy emerges in the deep generative representations for scene synthesis"
    - "on the steerability of generative adversarial networks"
    - "rpgan gans interpretability via random routing"
    - "interpreting the latent space of gans for semantic face editing"
    - "oogan disentangling gan with one hot sampling and orthogonal regularization"
    - "video to video synthesis"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "see attend and brake an attention based saliency map prediction model for end to end driving"):
  with members:
    - "salient object detection a survey"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "predicting driver attention in critical situations"
    - "learning where to attend like a human driver"
    - "end to end learning for self driving cars"
    - "deepgaze ii reading fixations from deep features trained on object recognition"

Connected through hyperedge HE-5 (induced by source paper "rethinking rgb d salient object detection models datasets and large scale benchmarks"):
  with members:
    - "super diffusion for salient object detection"
    - "adaptive fusion for rgb d salient object detection"
    - "salient object detection with lossless feature reflection and weighted structural loss"
    - "multi source weak supervision for saliency detection"
    - "saliency integration an arbitrator model"
    - "richer and deeper supervision network for salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "deep reasoning with multi scale context for salient object detection"

Connected through hyperedge HE-6 (induced by source paper "mining objects fully unsupervised object discovery and localization from a single image"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "salient object detection a survey"
    - "object part attention model for fine grained image classification"
    - "self produced guidance for weakly supervised object localization"
    - "adversarial complementary learning for weakly supervised object localization"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "diversified visual attention networks for fine grained object classification"
    - "deep descriptor transforming for image co localization"

Connected through hyperedge HE-7 (induced by source paper "instaboost boosting instance segmentation via probability map guided copy pasting"):
  with members:
    - "explicit shape encoding for real time instance segmentation"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "learning segmentation masks with the independence prior"
    - "srda generating instance segmentation annotation via scanning reasoning and domain adaptation"
    - "modeling visual context is key to augmenting object detection datasets"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "learning human object interactions by graph parsing neural networks"
    - "transferable interactiveness knowledge for human object interaction detection"

Connected through hyperedge HE-8 (induced by source paper "understanding human gaze communication by spatio temporal graph reasoning"):
  with members:
    - "deep visual attention prediction"
    - "iterative visual reasoning beyond convolutions"
    - "pairwise body part attention for recognizing human object interactions"
    - "learning human object interactions by graph parsing neural networks"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "situation recognition with graph neural networks"
    - "a simple neural network module for relational reasoning"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"

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)