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: "deep learning for object saliency detection and image segmentation"
  Abstract: In this paper, we propose several novel deep learning methods for object saliency detection based on the powerful convolutional neural networks. In our approach, we use a gradient descent method to iteratively modify an input image based on the pixel-wise gradients to reduce a cost function measuring the class-specific objectness of the image. The pixel-wise gradients can be efficiently computed using the back-propagation algorithm. The discrepancy between the modified image and the original one may be used as a saliency map for the image. Moreover, we have further proposed several new training methods to learn saliency-specific convolutional nets for object saliency detection, in order to leverage the available pixel-wise segmentation information. Our methods are extremely computationally efficient (processing 20-40 images per second in one GPU). In this work, we use the computed saliency maps for image segmentation. Experimental results on two benchmark tasks, namely Microsoft COCO and Pascal VOC 2012, have shown that our proposed methods can generate high-quality salience maps, clearly outperforming many existing methods. In particular, our approaches excel in handling many difficult images, which contain complex background, highly-variable salient objects, multiple objects, and/or very small salient objects.

Connected through hyperedge HE-1 (induced by source paper "suction grasp region prediction using self supervised learning for object picking in dense clutter"):
  with members:
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "grasp pose detection in point clouds"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "deep residual learning for image recognition"
    - "real time grasp detection using convolutional neural networks"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-2 (induced by source paper "supervised adversarial networks for image saliency detection"):
  with members:
    - "a deep learning based fast image saliency detection algorithm"
    - "energy based generative adversarial network"
    - "semantic segmentation using adversarial networks"
    - "improved techniques for training gans"
    - "deep identity aware transfer of facial attributes"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-3 (induced by source paper "automatic attribute discovery with neural activations"):
  with members:
    - "a deep learning based fast image saliency detection algorithm"
    - "understanding neural networks through deep visualization"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "deep carving discovering visual attributes by carving deep neural nets"
    - "visual affect around the world a large scale multilingual visual sentiment ontology"
    - "automatic concept discovery from parallel text and visual corpora"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"

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)