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: "pisa pixelwise image saliency by aggregating complementary appearance contrast measures with edge preserving coherence"
  Abstract: Driven by recent vision and graphics applications such as image segmentation and object recognition, computing pixel-accurate saliency values to uniformly highlight foreground objects becomes increasingly important. In this paper, we propose a unified framework called pixelwise image saliency aggregating (PISA) various bottom-up cues and priors. It generates spatially coherent yet detail-preserving, pixel-accurate, and fine-grained saliency, and overcomes the limitations of previous methods, which use homogeneous superpixel based and color only treatment. PISA aggregates multiple saliency cues in a global context, such as complementary color and structure contrast measures, with their spatial priors in the image domain. The saliency confidence is further jointly modeled with a neighborhood consistence constraint into an energy minimization formulation, in which each pixel will be evaluated with multiple hypothetical saliency levels. Instead of using global discrete optimization methods, we employ the cost-volume filtering technique to solve our formulation, assigning the saliency levels smoothly while preserving the edge-aware structure details. In addition, a faster version of PISA is developed using a gradient-driven image subsampling strategy to greatly improve the runtime efficiency while keeping comparable detection accuracy. Extensive experiments on a number of public data sets suggest that PISA convincingly outperforms other state-of-the-art approaches. In addition, with this work, we also create a new data set containing 800 commodity images for evaluating saliency detection.

Connected through hyperedge HE-1 (induced by source paper "se2net siamese edge enhancement network for salient object detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "semantic edge detection with diverse deep supervision"
    - "deep attentive tracking via reciprocative learning"
    - "salient object detection via high to low hierarchical context aggregation"
    - "reverse attention for salient object detection"
    - "casenet deep category aware semantic edge detection"
    - "richer convolutional features for edge detection"
    - "instance level salient object segmentation"

Connected through hyperedge HE-2 (induced by source paper "context aware saliency detection for image retargeting using convolutional neural networks"):
  with members:
    - "image retargetability"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "weakly and self supervised learning for content aware deep image retargeting"
    - "fast at fast automatic thumbnail generation using deep neural networks"
    - "pyramid scene parsing network"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "cat2000 a large scale fixation dataset for boosting saliency research"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "saliency detection with fully convolutional neural network"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "deep contrast learning for salient object detection"
    - "visual saliency based on multiscale deep features"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "contrast oriented deep neural networks for salient object detection"):
  with members:
    - "weakly supervised salient object detection using image labels"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "instance level salient object segmentation"
    - "deep contrast learning for salient object detection"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "recurrent attentional networks for saliency detection"
    - "visual saliency detection based on multiscale deep cnn features"

Connected through hyperedge HE-5 (induced by source paper "structure preserving image super resolution via contextualized multitask learning"):
  with members:
    - "attention aware face hallucination via deep reinforcement learning"
    - "perceptual losses for real time style transfer and super resolution"
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "factorized convolutional neural networks"
    - "local and holistic structure preserving image super resolution via deep joint component learning"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "human pose estimation from depth images via inference embedded multi task learning"

Connected through hyperedge HE-6 (induced by source paper "visual tracking via dynamic graph learning"):
  with members:
    - "correlation tracking via joint discrimination and reliability learning"
    - "fully convolutional siamese networks for object tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "learning spatially regularized correlation filters for visual tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "high speed tracking with kernelized correlation filters"
    - "staple complementary learners for real time tracking"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-7 (induced by source paper "context aware saliency map generation using semantic segmentation"):
  with members:
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "deep dense and low rank gaussian conditional random fields"):
  with members:
    - "semantic object parsing with graph lstm"
    - "deep contrast learning for salient object detection"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"
    - "semantic image segmentation via deep parsing network"
    - "visual saliency based on multiscale deep features"
    - "context aware cnns for person head detection"
    - "semantic object parsing with local global long short term memory"

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)