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 joint task learning for generic object extraction"
  Abstract: This paper investigates how to extract objects-of-interest without relying on hand-craft features and sliding windows approaches, that aims to jointly solve two sub-tasks: (i) rapidly localizing salient objects from images, and (ii) accurately segmenting the objects based on the localizations. We present a general joint task learning framework, in which each task (either object localization or object segmentation) is tackled via a multi-layer convolutional neural network, and the two networks work collaboratively to boost performance. In particular, we propose to incorporate latent variables bridging the two networks in a joint optimization manner. The first network directly predicts the positions and scales of salient objects from raw images, and the latent variables adjust the object localizations to feed the second network that produces pixelwise object masks. An EM-type method is presented for the optimization, iterating with two steps: (i) by using the two networks, it estimates the latent variables by employing an MCMC-based sampling method; (ii) it optimizes the parameters of the two networks unitedly via back propagation, with the fixed latent variables. Extensive experiments suggest that our framework significantly outperforms other state-of-the-art approaches in both accuracy and efficiency (e.g. 1000 times faster than competing approaches).

Connected through hyperedge HE-1 (induced by source paper "robust regression via deep negative correlation learning"):
  with members:
    - "residual non local attention networks for image restoration"
    - "bag of tricks for image classification with convolutional neural networks"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "residual dense network for image super resolution"
    - "image super resolution via dual state recurrent networks"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

Connected through hyperedge HE-2 (induced by source paper "non rigid object tracking via deep multi scale spatial temporal discriminative saliency maps"):
  with members:
    - "salient object detection a survey"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep saliency with encoded low level distance map and high level features"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "learning to track at 100 fps with deep regression networks"
    - "deeptrack learning discriminative feature representations online for robust visual tracking"
    - "visual saliency based on multiscale deep features"
    - "tracking randomly moving objects on edge box proposals"

Connected through hyperedge HE-3 (induced by source paper "one shot fine grained instance retrieval"):
  with members:
    - "faster r cnn features for instance search"
    - "deep image retrieval learning global representations for image search"
    - "localizing by describing attribute guided attention localization for fine grained recognition"
    - "mask cnn localizing parts and selecting descriptors for fine grained image recognition"
    - "a large scale car dataset for fine grained categorization and verification"
    - "subset feature learning for fine grained category classification"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"

Connected through hyperedge HE-4 (induced by source paper "a taxonomy of deep convolutional neural nets for computer vision"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "jointly modeling embedding and translation to bridge video and language"
    - "deep learning in neural networks"
    - "vqa visual question answering"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "data free parameter pruning for deep neural networks"
    - "deep convolutional inverse graphics network"

Connected through hyperedge HE-5 (induced by source paper "disc deep image saliency computing via progressive representation learning"):
  with members:
    - "a deep structured model with radius margin bound for 3d human activity recognition"
    - "visual saliency based on multiscale deep features"
    - "deep feature learning with relative distance comparison for person re identification"
    - "pisa pixelwise image saliency by aggregating complementary appearance contrast measures with edge preserving coherence"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "discriminatively trained and or graph models for object shape detection"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "action affect classification and morphing using multi task representation learning"):
  with members:
    - "learning multiple visual tasks while discovering their structure"
    - "domain generalization for object recognition with multi task autoencoders"
    - "multi task cnn model for attribute prediction"
    - "moddrop adaptive multi modal gesture recognition"
    - "sparse coding for multitask and transfer learning"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"
    - "learning task grouping and overlap in multi task learning"

Connected through hyperedge HE-7 (induced by source paper "robust optimization for deep regression"):
  with members:
    - "deep learning in neural networks"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "part based r cnns for fine grained category detection"
    - "matconvnet convolutional neural networks for matlab"
    - "r cnns for pose estimation and action detection"
    - "learning to generate chairs with convolutional neural networks"
    - "reading text in the wild with convolutional neural networks"
    - "depth map prediction from a single image using a multi scale deep network"

Connected through hyperedge HE-8 (induced by source paper "boosting convolutional features for robust object proposals"):
  with members:
    - "what makes for effective detection proposals"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "learning to segment moving objects in videos"
    - "return of the devil in the details delving deep into convolutional nets"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "caffe convolutional architecture for fast feature embedding"

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)