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: "exemplar based deep discriminative and shareable feature learning for scene image classification"
  Abstract: In order to encode the class correlation and class specific information in image representation, we propose a new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to hierarchically learn feature transformation filter banks to transform raw pixel image patches to features. The learned filter banks are expected to: (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative information; and (3) hierarchically extract patterns at different visual levels. Particularly, in each single layer of DDSFL, shareable filters are jointly learned for classes which share the similar patterns. Discriminative power of the filters is achieved by enforcing the features from the same category to be close, while features from different categories to be far away from each other. Furthermore, we also propose two exemplar selection methods to iteratively select training data for more efficient and effective learning. Based on the experimental results, DDSFL can achieve very promising performance, and it also shows great complementary effect to the state-of-the-art Caffe features.

Connected through hyperedge HE-1 (induced by source paper "deep patch learning for weakly supervised object classification and discovery"):
  with members:
    - "bayesian joint topic modelling for weakly supervised object localisation"
    - "hcp a flexible cnn framework for multi label image classification"
    - "deep filter banks for texture recognition description and segmentation"
    - "fast r cnn"
    - "relaxed multiple instance svm with application to object discovery"
    - "unsupervised object discovery and localization in the wild part based matching with bottom up region proposals"
    - "exploit bounding box annotations for multi label object recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "scene parsing with integration of parametric and non parametric models"):
  with members:
    - "dense recurrent neural networks for scene labeling"
    - "conditional random fields as recurrent neural networks"
    - "video tracking using learned hierarchical features"
    - "image parsing with a wide range of classes and scene level context"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-3 (induced by source paper "looking outside of the box object detection and localization with multi scale patterns"):
  with members:
    - "holistically nested edge detection"
    - "learning to detect vehicles by clustering appearance patterns"
    - "fast r cnn"
    - "convolutional channel features"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "what is holding back convnets for detection"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-4 (induced by source paper "multi scale volumes for deep object detection and localization"):
  with members:
    - "holistically nested edge detection"
    - "learning to detect vehicles by clustering appearance patterns"
    - "fast r cnn"
    - "convolutional channel features"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "what is holding back convnets for detection"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-5 (induced by source paper "no spare parts sharing part detectors for image categorization"):
  with members:
    - "object localization in imagenet by looking out of the window"
    - "mid level deep pattern mining"
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "spotlight the negatives a generalized discriminative latent model"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"

Connected through hyperedge HE-6 (induced by source paper "when naive bayes nearest neighbours meet convolutional neural networks"):
  with members:
    - "learning transferable features with deep adaptation networks"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "deep learning algorithms with applications to video analytics for a smart city a survey"):
  with members:
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "video tracking using learned hierarchical features"
    - "multi task cnn model for attribute prediction"
    - "scalable object detection using deep neural networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "pedestrian detection with unsupervised multi stage feature learning"

Connected through hyperedge HE-8 (induced by source paper "video tracking using learned hierarchical features"):
  with members:
    - "multi task cnn model for attribute prediction"
    - "building high level features using large scale unsupervised learning"

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)